The landscape of oncology research is rapidly evolving with the advent of artificial intelligence (AI), particularly in the design and execution of personalized clinical trials. Traditional oncology trials often suffer from limitations such as rigid protocols, inefficient patient recruitment, and population heterogeneity, which hinder the evaluation of targeted therapies. AI presents a transformative opportunity to overcome these challenges by enabling data-driven, adaptive, and personalized trial methodologies. This paper explores the integration of AI technologies across key areas of oncology trials, including patient stratification, adaptive trial designs, and predictive analytics for treatment response and toxicity. Machine learning algorithms can analyze genomic data, electronic health records, and other multidimensional datasets to identify suitable candidates for trials, enhancing both the efficiency and precision of patient recruitment. AI also supports real-time trial modifications, making adaptive designs more flexible and responsive to emerging data. Additionally, predictive models aid in forecasting treatment efficacy and potential adverse effects, contributing to safer and more effective therapeutic development. While challenges such as data standardization, regulatory acceptance, and algorithm transparency remain, the ongoing advancements in AI capabilities and collaborative frameworks offer a promising future for AI-driven personalized oncology trials. This paper underscores the potential of AI to revolutionize cancer research by aligning trial methodologies with the principles of precision medicine.