A Comprehensive Review of Machine Learning Applications in Laser Optics

Machine learning (ML) has emerged as a transformative tool in laser-based optics, driving advancements in both fundamental research and industrial applications. This review paper provides a comprehensive analysis of state-of-the-art ML-driven techniques across diverse laser technologies, including beam steering, optical tweezing, adaptive optics, mode-locking and pointing-acquisition-tracking to name among others. By leveraging deep learning, reinforcement learning, and other data-driven approaches, researchers have achieved real-time control, automated optimization, and enhanced system performance beyond the capabilities of traditional methods. The paper categorizes current ML applications in laser optics, addressing challenges such as dynamic aberration correction, precision beam shaping, and high-throughput data interpretation, with illustrative examples including intelligent surgical laser systems, defect-free optical trapping, and self-correcting adaptive optics. Emerging trends such as hybrid ML models, edge computing-based real-time feedback loops, and the development of open-source datasets and benchmarking tools are discussed, underscoring the potential of ML to propel laser-based optical systems to unprecedented levels of precision, efficiency, and adaptability.