Laser-assisted manufacturing processes such as welding, cutting, surface processing, and additive manufacturing demand precise control of energy delivery and material interaction to ensure consistent quality and process stability. Conventional monitoring and control strategies often rely on predefined models and fixed thresholds, which struggle to adapt to process variability, complex dynamics, and real-time disturbances. Recent advances in machine learning (ML) have introduced a paradigm shift, enabling laser systems to acquire a “new sense and brain” through intelligent monitoring and closed-loop control.This comprehensive review surveys the state of the art in machine learning–based real-time monitoring and adaptive control for laser-assisted processes. The paper systematically examines sensor technologies, including optical, thermal, acoustic, and spectroscopic sensing, and their integration with data-driven models for feature extraction, defect detection, and process state estimation. Supervised, unsupervised, and deep learning approaches are reviewed in the context of real-time process monitoring, anomaly detection, and quality prediction. Furthermore, emerging closed-loop control frameworks that leverage ML for adaptive adjustment of laser parameters—such as power, scan speed, pulse duration, and beam shaping—are critically analyzed.Key challenges related to data availability, model generalization, real-time deployment, and industrial scalability are discussed, along with recent solutions such as hybrid physics-informed learning, transfer learning, and edge computing. Finally, future research directions are outlined to accelerate the adoption of intelligent laser systems capable of autonomous decision-making. This review highlights how adaptive machine learning is transforming laser-assisted manufacturing toward smarter, more robust, and self-optimizing processes.