Biosensing systems have evolved from basic laboratory devices to advanced platforms that measure specific biological molecules. These devices offer benefits like low cost, speed, and portability, making them useful for health monitoring and environmental applications. The integration of machine learning (ML) and artificial intelligence (AI) enhances the functionality of biosensors, improving data analysis and decision-making. This technology aids in real-time monitoring of diseases and supports personalized healthcare by analyzing data from various biofluids. Additionally, connecting biosensors to smartphones has expanded their accessibility, especially in rural areas. Despite the advancements, further research is needed to overcome challenges and commercialize smarter biosensors for broader applications. Overall, AI enhances the accuracy and effectiveness of biosensing, allowing for better health diagnostics and environmental monitoring. We have examined the progress and challenges in developing biosensing systems, along with the significant impact of AI and machine learning. In conclusion, when proper precautions and safe conditions are maintained, biosensing systems can effectively measure concentrations of specific biological molecules. Moreover, advancements in nanotechnology and AI have greatly enhanced the functionality and applications of these systems.