Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite their advantages, these processes are inherently complex and are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 11 relevant articles sourced from the Scopus and IEEE Xplore databases. The ultimate aim of this work is to contribute to the advancement of autonomous manufacturing systems - capable of self-monitoring and self-correction to ensure optimal part quality, enhanced efficiency, and reduced human intervention. By defining a groundwork for future developments, this review not only highlights current advancements but also paves the way for continued innovation and progress to develop AI-based controllers in manufacturing.