Cancer continues to be a major global health challenge, with high rates of morbidity and mortality. Traditional chemotherapy regimens often overlook individual patient variability, leading to suboptimal outcomes and significant side effects. This paper presents the application of Reinforcement Learning (RL) and Decision Transformers (DT) for developing personalized chemotherapy strategies. By leveraging offline data and simulated environments, our approach dynamically adjusts dosing strategies based on patient responses, optimizing therapeutic efficacy while minimizing toxicity. Experimental results show that DTs outperform both traditional Constant Dose Regimens (CDR) and online training methods like Proximal Policy Optimization (PPO), leading to improved survival times and reduced mortality. Our findings highlight the potential of RL and DTs to revolutionize cancer treatment by offering more effective and personalized therapeutic options.