Optical sensor technologies are central to the advancement of Internet of Things (IoT) ecosystems and smart systems. Among these, photodetectors—a key subset of optical sensors—find extensive applications in areas such as smart sensing, home automation, autonomous vehicles, wearable electronics, and AI-driven platforms. The demand for highly sensitive, cost-effective, flexible, and lightweight photodetectors continues to grow, especially for seamless integration with CMOS processors and wearable systems. However, developing high-performance photodetectors presents several challenges, including optimizing device lifetime, operational speed, and selecting efficient material systems. Addressing these issues requires a combination of comprehensive literature review, fabrication process optimization, and detailed material characterization. In recent years, machine learning (ML) techniques have emerged as powerful tools to overcome these limitations. ML not only enhances fabrication efficiency by reducing processing time, cost, and human intervention, but also plays a crucial role in predicting material behavior, optimizing device parameters, and accelerating design cycles. By leveraging ML, researchers can gain deeper insights into material–device interactions and significantly improve device performance. This chapter presents a comprehensive overview of the latest developments in integrating machine learning into photodetector design. It covers the foundational concepts of ML, classifications and operating principles of various photodetector types, and illustrates how ML contributes to performance enhancement, smart design, and real-time optimization in modern photodetector systems.