Artificial intelligence (AI) integrated with electrochemical biosensors is transforming medical treatments by enhancing patient data collection and enabling advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are highly effective. AI is adept at identifying, categorizing, characterizing, and projecting complex data patterns. As IoT, big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. However, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology. Electrochemical biosensors are analytical tools that identify and measure target analytes by combining biological recognition components with electrochemistry. They are used in drug research, environmental pollutants, food safety, and medical diagnostics due to their high sensitivity, specificity, and quick reaction times. Challenges include data interpretation, integration, power consumption, user acceptance, regulatory compliance, and cost-effectiveness. Recent advances in electrochemical biosensing, such as electrode substrate fabrication, miniaturization, materials science, digitalization, and artificial intelligence, have transformed our understanding of the field. These biosensors identify and measure certain analytes in a sample by combining biological recognition components with electrochemical methods. The application of AI to improve the functionality and performance of electrochemical biosensors is an area of advancement. Researchers aim to increase the sensitivity, selectivity, and overall accuracy of electrochemical biosensors by incorporating AI into them. The detection and monitoring of hydrogen energy is one particular application where the integration of AI has demonstrated potential. Improvement of the performance and accuracy of electrochemical biosensors by altering the nickel electrode used for electrochemical hydrogen energy detection. The redesigned nickel electrode detects hydrogen energy with greater sensitivity and selectivity, allowing for more precise measurements. Furthermore, the incorporation of AI into electrochemical biosensors enables data processing and pattern identification, resulting in real-time monitoring and prediction capabilities. This combination of modern technology and electrochemical biosensors has the potential to transform the field of hydrogen energy detection by providing a more precise and efficient means of monitoring hydrogen energy levels. AI-powered electrochemical biosensors improve sensing performance. AI algorithms have greatly enhanced the sensitivity and selectivity of electrochemical biosensors. Artificial intelligence may extract useful information from complex electrochemical signals using modern data processing techniques, resulting in increased detection limits and accuracy. Thus, one of the major advancements is the integration of AI with electrochemical biosensors for real-time monitoring of biological analytes. AI algorithms can continually examine data streams from biosensors, allowing for rapid detection of changes inArtificial intelligence (AI) integrated with electrochemical biosensors is transforming medical treatments by enhancing patient data collection and enabling advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are highly effective. AI is adept at identifying, categorizing, characterizing, and projecting complex data patterns. As IoT, big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. However, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology. Electrochemical biosensors are analytical tools that identify and measure target analytes by combining biological recognition components with electrochemistry. They are used in drug research, environmental pollutants, food safety, and medical diagnostics due to their high sensitivity, specificity, and quick reaction times. Challenges include data interpretation, integration, power consumption, user acceptance, regulatory compliance, and cost-effectiveness. Recent advances in electrochemical biosensing, such as electrode substrate fabrication, miniaturization, materials science, digitalization, and artificial intelligence, have transformed our understanding of the field. These biosensors identify and measure certain analytes in a sample by combining biological recognition components with electrochemical methods. The application of AI to improve the functionality and performance of electrochemical biosensors is an area of advancement. Researchers aim to increase the sensitivity, selectivity, and overall accuracy of electrochemical biosensors by incorporating AI into them. The detection and monitoring of hydrogen energy is one particular application where the integration of AI has demonstrated potential. Improvement of the performance and accuracy of electrochemical biosensors by altering the nickel electrode used for electrochemical hydrogen energy detection. The redesigned nickel electrode detects hydrogen energy with greater sensitivity and selectivity, allowing for more precise measurements. Furthermore, the incorporation of AI into electrochemical biosensors enables data processing and pattern identification, resulting in real-time monitoring and prediction capabilities. This combination of modern technology and electrochemical biosensors has the potential to transform the field of hydrogen energy detection by providing a more precise and efficient means of monitoring hydrogen energy levels. AI-powered electrochemical biosensors improve sensing performance. AI algorithms have greatly enhanced the sensitivity and selectivity of electrochemical biosensors. Artificial intelligence may extract useful information from complex electrochemical signals using modern data processing techniques, resulting in increased detection limits and accuracy. Thus, one of the major advancements is the integration of AI with electrochemical biosensors for real-time monitoring of biological analytes. AI algorithms can continually examine data streams from biosensors, allowing for rapid detection of changes in