Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Abstract In the evolving landscape of the Internet of Things (IoT), the deployment of sensors is surging, along with increasing demands for higher performance. However, improving sensor capabilities solely through hardware advancements, such as material and structural design, faces inherent limitations. Common sensing materials, including semiconducting metal oxides, graphene, conductive polymers, elastomers, and noble metals, suffer from issues such as poor selectivity, slow response time, and low resolution, which hinder their practical applications. To address these challenges, recent efforts focus on leveraging machine learning (ML) for sensor signal processing, enhancing performance beyond conventional hardware‐based approaches. This review explores the role of ML in advancing next‐generation physical and chemical sensors. It examines how ML‐driven signal processing enhances key sensor attributes, such as selectivity, response time, spatial resolution, stability, power consumption, and analysis process. Additionally, widely adopted ML techniques are systematically categorized based on their targeted performance improvements, and promising strategies to overcome existing bottlenecks are discussed. The review also highlights potential applications of ML‐enhanced sensors, providing insights into their commercialization prospects. By presenting a structured analysis and future outlook, this paper aims to support the continued integration of ML into sensor technology and inspire further research in this rapidly evolving field.