As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved Proximal Policy Optimization for Disassembly Line(PPO-DL) approach to address Multi-objective Disassembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MDLBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPODL’s superiority in finding near-optimal solutions across various scenarios.