Machine learning (ML) integrated with biosensors has significantly enhanced the capabilities of point-of-care testing (PoCT) in modern healthcare. This review explores the role of ML in improving biosensors for PoCT, focusing on their applications in electrochemical, lab-on-a-chip, electrochemiluminescence/chemiluminescence, colorimetric, and wearable sensors. ML algorithms improve the sensitivity, accuracy, and speed of diagnostic procedures by processing complex biological data, enabling early disease detection and reducing false positives and negatives. The integration of ML with biosensors allows for real-time monitoring, personalized treatment, and prompt responses to medical emergencies, making it a cornerstone of future healthcare. ML models, including supervised, unsupervised, and reinforcement learning, are used to classify and regress data, enhancing the diagnostic capabilities of biosensors. Supervised learning models, such as linear regression, decision trees, random forests, k-nearest neighbors, support vector machines, naive Bayes, and artificial neural networks, are particularly effective in improving diagnostic accuracy. These models are applied in various PoCT applications, including the detection of biomarkers like glucose, insulin, and SARS-CoV-2 variants, as well as in the identification of pathogens and cancer markers. ML-assisted electrochemical sensors, such as those for lidocaine and glucose detection, demonstrate high sensitivity and accuracy. Colorimetric sensors, aided by ML, enable rapid and selective detection of various analytes with minimal equipment. Lab-on-a-chip sensors, integrated with ML, provide high-throughput screening and automated data interpretation, reducing healthcare costs and improving accessibility. Wearable sensors, combined with ML, allow for real-time monitoring of vital signs, enabling early detection of health issues and personalized healthcare. ML-assisted electrochemiluminescence/chemiluminescence systems and electronic-nose (E-Nose) technologies further enhance diagnostic capabilities by detecting volatile organic compounds and other biomarkers. These technologies are applied in various healthcare contexts, including diabetes detection, lung cancer diagnosis, and food quality control. Despite their advantages, challenges such as data processing, model optimization, and integration with clinical settings must be addressed to fully realize the potential of ML in PoCT. Overall, the integration of ML with biosensors is transforming PoCT into a more efficient, accurate, and accessible diagnostic tool for modern healthcare.Machine learning (ML) integrated with biosensors has significantly enhanced the capabilities of point-of-care testing (PoCT) in modern healthcare. This review explores the role of ML in improving biosensors for PoCT, focusing on their applications in electrochemical, lab-on-a-chip, electrochemiluminescence/chemiluminescence, colorimetric, and wearable sensors. ML algorithms improve the sensitivity, accuracy, and speed of diagnostic procedures by processing complex biological data, enabling early disease detection and reducing false positives and negatives. The integration of ML with biosensors allows for real-time monitoring, personalized treatment, and prompt responses to medical emergencies, making it a cornerstone of future healthcare. ML models, including supervised, unsupervised, and reinforcement learning, are used to classify and regress data, enhancing the diagnostic capabilities of biosensors. Supervised learning models, such as linear regression, decision trees, random forests, k-nearest neighbors, support vector machines, naive Bayes, and artificial neural networks, are particularly effective in improving diagnostic accuracy. These models are applied in various PoCT applications, including the detection of biomarkers like glucose, insulin, and SARS-CoV-2 variants, as well as in the identification of pathogens and cancer markers. ML-assisted electrochemical sensors, such as those for lidocaine and glucose detection, demonstrate high sensitivity and accuracy. Colorimetric sensors, aided by ML, enable rapid and selective detection of various analytes with minimal equipment. Lab-on-a-chip sensors, integrated with ML, provide high-throughput screening and automated data interpretation, reducing healthcare costs and improving accessibility. Wearable sensors, combined with ML, allow for real-time monitoring of vital signs, enabling early detection of health issues and personalized healthcare. ML-assisted electrochemiluminescence/chemiluminescence systems and electronic-nose (E-Nose) technologies further enhance diagnostic capabilities by detecting volatile organic compounds and other biomarkers. These technologies are applied in various healthcare contexts, including diabetes detection, lung cancer diagnosis, and food quality control. Despite their advantages, challenges such as data processing, model optimization, and integration with clinical settings must be addressed to fully realize the potential of ML in PoCT. Overall, the integration of ML with biosensors is transforming PoCT into a more efficient, accurate, and accessible diagnostic tool for modern healthcare.