The convergence of artificial intelligence (AI) and CRISPR-Cas gene editing represents a paradigm shift in cancer therapeutics, enabling unprecedented precision in targeting oncogenic mutations while minimizing off-target effects. This integration addresses critical limitations of traditional gene editing approaches through computational optimization and predictive modeling. We learn how machine learning algorithms enhance CRISPR applications in oncology, from guiding RNA design to therapeutic delivery and patient monitoring, highlighting current clinical applications and future therapeutic potential. On reviewing current AI-CRISPR integration strategies, including deep learning models for guide RNA optimization (DeepCRISPR, BE-Hive), computational prediction of off-target effects, and AI-driven CAR-T cell engineering. Analysis encompasses ongoing clinical trials for hematologic malignancies and emerging solid tumor applications also. AI-enhanced CRISPR systems demonstrated significant improvements in editing efficiency and specificity. Machine learning models achieve >90% accuracy in predicting guide RNA effectiveness while reducing off-target events by 60-80%. Clinical trials show promising outcomes like the AI-designed CAR-T therapies showed enhanced tumor recognition and reduced cytokine release syndrome. Emerging applications include base editing for KRAS G12C mutations, prime editing for precise corrections, and multiplexed editing for combination cancer gene therapy. The AI-CRISPR synergy is revolutionizing cancer treatment through personalized therapeutic design, real-time treatment optimization, and predictive resistance modeling. Current challenges include delivery optimization, regulatory frameworks, and ethical considerations surrounding germline editing. Future directions encompass foundation models for biological design, automated experimental workflows, and integration with liquid biopsy technologies for dynamic treatment adaptation. These advances promise to transform cancer care from broad-spectrum treatments to molecularly-precise, AI-guided therapies tailored to individual tumor genetics, potentially improving outcomes while reducing toxicity across diverse cancer types.

Srinivasa Tejaswini Adada is a distinguished Medical Oncologist, Hematologist, and BMT Physician, specializing in translational cancer research and precision oncology. As Co-founder & Director of Cancer Conscious, MEDHIC.AI, Fifth Care Solutions Pvt.ltd, Her leads groundbreaking innovations in AI-driven oncology solutions, developing digital health platforms that enhance therapeutic outcomes and advance personalized medicine approaches. Her work represents a paradigm shift toward data-informed cancer treatment protocols. With advanced executive education from Indian School of Business in healthcare management, Oxford Saïd Business School, and Cambridge Centre of Innovation and Development, she brings unique expertise spanning clinical oncology, healthcare innovation, and strategic research leadership. She actively contributes to global cancer research through her memberships in ESMO, ASCO, ISMPO, and ISBMT, collaborating on international initiatives that advance cancer science frontiers. Her multidisciplinary approach encompasses clinical trial supervision, biomarker research, and next-generation therapeutic interventions. Her current focus integrates precision oncology, immunotherapy innovations, and AI-enhanced diagnostic platforms to revolutionize cancer treatment paradigms, positioning her as a leader in technology-driven, evidence-based cancer care that aims to improve global patient outcomes through cutting-edge research and clinical excellence.