This review explores the synergy between modified electrodes and artificial intelligence (AI), emphasizing their combined potential to advance electrochemical applications through data-driven approaches. AI technologies, including machine learning (ML) and artificial neural networks (ANN), have significantly enhanced the performance of sensors, particularly in terms of sensitivity, accuracy, and efficiency. The integration of AI into electrochemical sensors, biosensors, and portable diagnostic devices has enabled real-time data analysis, improved diagnostic accuracy, and reduced costs and experimental time. Furthermore, AI has streamlined the design of sensors, making them more adaptable and context-aware, suitable for applications in healthcare, environmental monitoring, and agriculture. The incorporation of TinyML into portable devices has also facilitated the development of low-power sensors capable of real-time processing. This review highlights the transformative impact of AI on sensor technology and discusses the future implications for point-of-care diagnostics, ecological monitoring, and personalized healthcare. The continued evolution of AI-enhanced sensors is expected to drive innovations in both scientific research and industrial applications, making diagnostic tools more accessible and improving the efficiency of global environmental management.