Recommender systems for cancer patients utilize machine learning (ML) algorithms and data analytics to deliver personalized treatment alternatives, lifestyle suggestions, and access to clinical trials. These systems amalgamate electronic health records (EHRs), genetic profiles, and clinical guidelines to aid oncologists in making educated decisions and enhancing patient outcomes. Our study assessed the efficacy of a cancer care recommender system with a dataset of 5000 patient records, measuring critical metrics like treatment suggestion accuracy (87.5%), enhancement in patient adherence (22%), and a decrease in oncologist decision-making time (30%). Our findings underscore the capability of such tools to enhance treatment precision, mitigate information overload, and facilitate evidence-based practice. Nonetheless, issues like data incompleteness, algorithmic bias, and adoption barriers persist as substantial problems. Findings demonstrate that data standardization and regional adaptation can raise model performance by 15% in marginalized populations, whilst explainability and privacy-preserving technologies bolster trust and user engagement. Future advancements must prioritize interpretable AI, real-time data integration, and regulatory compliance to enhance the efficacy of recommender systems in individualized cancer treatment. Future advancements in recommender systems for cancer care must also address ethical considerations, including informed consent, transparency in AI-driven recommendations, and patient data privacy, to ensure trust, accountability, and equitable access to personalized treatments.