The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessments directly at the patient level. This review explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects. It provides an overview of core biosensing technologies and their use in POC, highlighting issues that may be solved with AI. The paper discusses AI methodologies such as machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. It also explores the applications of AI at each stage of the biosensor development process, including analyte selection, recognition element selection, transduction, and data handling. The review includes a thorough analysis of outstanding challenges in AI-assisted biosensing, focusing on technical and ethical issues such as data security, algorithmic bias, and regulatory compliance. The authors aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessments directly at the patient level. This review explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects. It provides an overview of core biosensing technologies and their use in POC, highlighting issues that may be solved with AI. The paper discusses AI methodologies such as machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. It also explores the applications of AI at each stage of the biosensor development process, including analyte selection, recognition element selection, transduction, and data handling. The review includes a thorough analysis of outstanding challenges in AI-assisted biosensing, focusing on technical and ethical issues such as data security, algorithmic bias, and regulatory compliance. The authors aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.