Artificial intelligence (AI) is reshaping precision oncology by enabling earlier tumor detection, high-resolution molecular profiling, and personalized immunotherapy optimization. Rapid advances in deep learning, multimodal data integration, and large-scale biomedical datasets have created opportunities to move beyond traditional organ-based cancer management toward dynamic, biomarker-driven care pathways. This manuscript synthesizes recent translational evidence on AI applications across the oncology continuum, emphasizing three interconnected domains: early tumor detection, molecular characterization, and immunotherapy response prediction. AI-enhanced imaging and digital pathology models demonstrate improved sensitivity for early-stage malignancies and premalignant lesions, while liquid biopsy analytics and multi-cancer early detection algorithms show promise in identifying minimal residual disease. In parallel, machine learning approaches applied to genomics, transcriptomics, and spatial tumor microenvironment data are refining biomarker discovery and therapeutic stratification. For immunotherapy, predictive models integrating tumor mutational burden, immune infiltration patterns, and longitudinal clinical data support optimized patient selection, toxicity forecasting, and adaptive treatment strategies. Despite these advances, challenges remain in external validation, bias mitigation, regulatory oversight, data governance, and equitable deployment. A coordinated translational framework combining rigorous validation, human-in-the-loop clinical decision support, and adaptive monitoring will be essential to ensure safe and effective integration of AI into oncology practice.