With the advancement of intelligent manufacturing, additive manufacturing (AM) has emerged as a key enabling technology, its nesting and scheduling optimization directly impact resource utilization and delivery efficiency. These problems involve part layout, task allocation, and time sequencing, characterized by strong coupling, multiple constraints, and multi-objective complexity. This paper systematically reviews the research progress in AM nesting and scheduling from the perspectives of problem definition, model constraints, optimization objectives, and algorithmic approaches. Although various methods such as mathematical programming, heuristics, and intelligent optimization have been applied, challenges remain in terms of real-world adaptability, system integration, algorithm innovation, and sustainability. Future research should emphasize intelligent integration, multi-objective coordination, and green manufacturing to drive AM nesting and scheduling toward higher efficiency, intelligence, and sustainability.