This thesis addresses the Dynamic Job Shop Scheduling Problem (DJSSP), a critical challenge in modern manufacturing characterized by unpredictable arrivals, strict deadlines, and routing flexibility. Traditional heuristics often lack adaptability in such dynamic environments. To overcome these limitations, a reinforcement learning framework based on Maskable Proximal Policy Optimization (PPO) is proposed. Key features include multi-discrete action spaces for parallel machine decisions, action masking for feasibility, and a multi-objective reward function balancing lateness reduction, on-time delivery, throughput, and operational stability. Domain randomization during training enhances generalization across varying conditions. A real case study validates the framework, comparing two PPO agents with classical heuristics (EDD, FIFO, LPT, SLACK) and a metaheuristic (Genetic Algorithm).