Laser and electron beam powder bed fusion of metals (PBF-LB/M and PBF-EB/M) processes are among the most widely used metal Additive Manufacturing (AM) technologies, offering unprecedented design freedom by enabling the fabrication of complex geometries that are impossible or prohibitively expensive to produce using conventional manufacturing methods. However, ensuring consistent part quality remains a significant challenge, as defects such as lack of fusion and keyhole porosity, cracks, surface irregularities, and dimensional inaccuracies can arise from suboptimal process parameters. Traditional parameter optimization aimed at minimizing such defects relies on extensive experimental campaigns, which are costly, time-consuming, and often material-specific. Moreover, the optimized process parameters must not only suppress defect formation but also maintain high productivity and cost-efficient processing to meet industrial manufacturing requirements. Recently, machine learning (ML) approaches have emerged as powerful tools to model relationships between PBF-LB/EB process parameters and resulting defects. However, ML models typically require large datasets that are not available for newly developed alloys. Transfer learning (TL) provides a pathway to address this limitation. In essence, transfer learning refers to the reuse of knowledge gained from one domain (e.g., a well-characterized alloy with extensive process data) to improve learning performance in a related but data-scarce domain (e.g., a newly developed alloy). Instead of training a model from scratch, TL adapts pre-trained models by fine-tuning them using a limited amount of new data. This review summarizes recent advances in ML and TL approaches for PBF-LB/EB process optimization, with a particular focus on their role in improving part quality and enabling efficient parameter development for emerging alloy systems.