Oncology treatment faces significant challenges due to patient-specific variability and tumor heterogeneity. Dynamic personalization, driven by real-time data, holds the potential to optimize cancer therapy for individual patients. This abstract explores the application of deep reinforcement learning (DRL) in real-time treatment adaptation. DRL, an artificial intelligence technique, uses continuous feedback from a patient’s biological and clinical data to tailor treatment strategies dynamically. By leveraging large datasets and predictive modeling, DRL can adapt therapeutic approaches to changing conditions, improving outcomes while minimizing adverse effects. This approach can help oncologists determine the most effective drug combinations, dosages, and treatment schedules based on evolving patient responses. The integration of DRL into oncology promises a shift from static, one-size-fits-all protocols to personalized, adaptive strategies, leading to more efficient and precise cancer treatment paradigms. This work opens new avenues for AI-driven innovations in healthcare, particularly in precision oncology.