Artificial Intelligence‐Driven Innovations in Electrochemical Sensor Design and Application

Abstract The deep integration of electrochemical sensors and artificial intelligence ( AI ) is driving the evolution of analytical science toward intelligence. Aiming at the bottlenecks of traditional electrochemical sensors in the separation of complex matrix signals, the selectivity of multi‐targets detection, and the processing of massive data, this article systematically summarizes the innovative application of AI technology in sensor signal processing, pattern recognition, and performance optimization. By integrating the synergistic effect of algorithms such as machine learning and novel nanomaterials (MXenes, MOFs, and nanozymes), the AI‐driven electrochemical sensors significantly enhance the detection sensitivity and selectivity, realizing the precise identification and quantitative analysis of targets in complex samples. Current technology has made breakthroughs, but the data dependence of AI models, algorithm interpretability, and lack of standardization still need to be broken through. Future research needs to focus on the optimization of feature extraction algorithms, the construction of standardized databases, the development of interpretable models, and data privacy protection, in order to promote the development of a new generation of sensing systems with high sensitivity, high intelligence, and high robustness.