Laser beam welding manufacturing (LBW), being a promising joining technology with superior capabilities of high-precision, good-flexibility and deep penetration, has attracted considerable attention over the academic and industry circles. To date, the lack of repeatability and stability are still regarded as the critical technological barrier that hinders its broader applications especially for high-value products with demanding requirements. One significant approach to overcome this formidable challenge is in-situ monitoring combined with artificial intelligence (AI) techniques, which has been explored by great research efforts. The main goal of monitoring is to gather essential information on the process and to improve the understanding of the occurring complicated weld phenomena. This review firstly describes ongoing work on the in-situ optical sensing, behavior characterization and process modeling during dynamic LBW process. Then, much emphasis has been placed on the optical radiation techniques, such as multi-spectral photodiode, spectrometer, pyrometer and high-speed camera for observing the laser physical phenomenon including melt pool, keyhole and vapor plume. In particular, the advanced image/signal processing techniques and machine-learning models are addressed, in order to identify the correlations between process parameters, process signatures and product qualities. Finally, the major challenges and potential solutions are discussed to provide an insight on what still needs to be achieved in the field of process monitoring for metal-based LBW processes. This comprehensive review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they improve and control the welding quality.