Traditional approaches to anticancer dosing typically rely on fixed protocols that often overlook how patients respond differently to treatment. This can limit both effectiveness and safety. In this work, we introduce a digital twin–reinforcement learning (DT-RL) framework designed to support more adaptive and personalized treatment strategies. The system brings together physiologically based pharmacokinetic (PBPK) modeling, tumor–immune dynamics, and a safety-aware reinforcement learning agent within a closed-loop setup that continuously updates decisions based on the patient’s evolving condition. Using a high-fidelity simulation environment informed by recent progress in radiopharmaceuticals, chemotherapy, and nanoparticle-based drug delivery, the framework enables real-time prediction of how drugs distribute in the body and how tumors respond. By combining mechanistic PBPK models with Neural Ordinary Differential Equations and physics-informed neural networks, the approach improves prediction across multiple biological scales. Within this setup, the RL agent adjusts dosing over time, aiming to maximize treatment effectiveness while keeping toxicity within safe limits. Across representative applications—including PSMA-targeted radiopharmaceutical therapy, triple-negative breast cancer chemotherapy, and nanoparticle-based delivery systems—the framework shows encouraging performance. It achieves dose prediction errors in the range of 8–20%, reaches around 85% accuracy in response prediction, and improves tumor targeting compared to conventional approaches. There are still important challenges to address, particularly around clinical validation, real-time data integration, and regulatory considerations. Even so, this work outlines a practical path toward more responsive and individualized cancer treatment, where therapy can be continuously adjusted based on patient-specific dynamics rather than fixed dosing rules.