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Abstract Introduction: Achieving equity in translational precision medicine requires the integration of genomic, clinical, and social determinants of health (SDoH) data to uncover disease mechanisms, personalize treatment, and reduce health disparities. Yet, existing bioinformatics tools are often hindered by fragmented data structures, steep technical barriers, and limited capacity to incorporate SDoH variables-challenges that disproportionately affect underserved populations. To address this, we developed AI-HOPE-PM (Artificial Intelligence agent for High-Optimization and Precision mEdicine in Population Metrics), a conversational AI platform that allows users to conduct multi-dimensional cancer analyses through natural language interaction. By unifying large-scale clinical, genomic, and SDoH data within a dynamic and accessible interface, AI-HOPE-PM lowers the barrier to integrative research and supports inclusive, hypothesis-driven investigation. Methods: AI-HOPE-PM leverages large language models (LLMs), structured natural language processing, retrieval-augmented generation (RAG), and an internal Python-based workflow engine to automate data ingestion, filtering, cohort stratification, and statistical analysis. The platform operates on harmonized datasets from TCGA, cBioPortal, and AACR GENIE, enriched with simulated SDoH variables such as financial strain, food insecurity, and healthcare access. Free-text queries (e.g., Compare survival outcomes in CRC patients with TP53 mutations and limited access to care) are parsed into executable scripts aligned with biomedical ontologies. The system performs survival modeling, odds ratio testing, and case-control comparisons, generating interpretable visualizations and narrative reports in real time. Benchmarking against platforms like cBioPortal and UCSC Xena demonstrated 92.5% query interpretation accuracy and efficient performance across both CPU and GPU cloud environments. Results: AI-HOPE-PM successfully translated diverse user queries into real-time, executable analyses across colorectal cancer (CRC) datasets, enabling integration of clinical, genomic, and SDoH data. In one case study, the platform identified significantly worse survival in FOLFOX-treated CRC patients with TP53 mutations experiencing financial strain (p = 0.0481). Another analysis revealed poorer progression-free survival in APC wild-type patients with good healthcare access (p = 0.0233). Additional findings highlighted the influence of social support (p = 0.0220), food insecurity (p = 0.0162), and health literacy on outcomes and treatment access. Odds ratio analyses revealed disparities in chemotherapy exposure (OR = 0.356 for food-insecure patients) and KRAS mutation prevalence by sex and literacy status. AI-HOPE-PM also surfaced racial and ethnic differences in progression-free survival, emphasizing the importance of SDoH integration in population-level cancer research. All analyses were completed in under one minute, significantly reducing manual workload and improving scalability. Conclusions: AI-HOPE-PM marks a significant leap forward in the field of precision oncology by uniting clinical, genomic, and SDoH data within a single, conversational AI framework. Instead of relying on traditional, code-heavy approaches, the platform enables users to perform complex, multi-layered analyses through simple natural language interactions. This functionality not only democratizes access to integrative cancer research but also enhances the ability to uncover disparities in outcomes linked to genetic, clinical, and social variables. By contextualizing molecular insights within real-world social environments, AI-HOPE-PM delivers a more comprehensive understanding of cancer biology and care inequities. Its high performance, interpretability, and scalability position it as a powerful tool for accelerating hypothesis generation, guiding biomarker discovery, and informing equity-driven treatment strategies. As a flexible and user-centered platform, AI-HOPE-PM lays the groundwork for a new paradigm in AI-assisted, health equity-focused translational research.