Healthcare, constituting approximately 10% of the global GDP, faces transformative opportunities through technological innovations, particularly data-driven methods and computational power. Biosensors, capable of detecting biological and chemical analytes, have gained attention in academic circles. However, conventional biosensors have limitations, which can be overcome by integrating machine learning (ML) algorithms. Electrochemical biosensors, in particular, have evolved from traditional to wearable forms, offering applications in healthcare, disease diagnosis, and environmental monitoring. AI-enhanced biosensors, known as AI-biosensors, enable early disease diagnosis, bacteria detection, and hazardous compound measurement. Machine learning optimizes biosensor performance and enhances parameters like electrode design and analyte selection. The use of machine learning in biosensors has witnessed significant growth, facilitating efficient data analysis. This fusion of artificial intelligence with biosensors bridges the gap between data acquisition and analysis, resulting in enhanced diagnostic precision. This evaluation reviews the use of ML in conjunction with non-invasive biosensors, highlighting their potential to revolutionize healthcare and medicine. Future research directions and opportunities are also discussed.