• Rishi Reddy Kothinti

Using body immunity through personalized immunotherapy represents an innovative medical treatment for stronger tumor cell engagement. ML technology developments recently led to substantial enhancements in immunotherapeutic precision rates and operational efficiency throughout all therapeutic steps, including biomarker designation, patient classification, and response prediction. The current applications of ML in immunotherapy serve as the primary topic of this paper through research investigations on deep learning genomic models with reinforcement learning adaptive treatments and AI-based drug development procedures. The paper examines implementation hurdles healthcare practitioners face when adopting ML by discussing barriers such as restricted data sources, impractical models, and regulation requirements. Multi-dimensional data unification and continuous patient surveillance represent the future direction of ML-enhanced immunotherapeutic performance according to the final sections of the study. Personalized cancer immunotherapy received a revolutionary boost through ML because it combines powerful computation with biological data to develop innovative solutions that yield better patient survival rates.

Machine Learning, Cancer Immunotherapy, Personalized Medicine, Biomarker Discovery, AI in Healthcare

"THE ROLE OF MACHINE LEARNING IN PERSONALIZED CANCER IMMUNOTHERAPY: CURRENT TRENDS AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.q459-q474, April-2024, Available :http://www.jetir.org/papers/JETIR2404H57.pdf

"THE ROLE OF MACHINE LEARNING IN PERSONALIZED CANCER IMMUNOTHERAPY: CURRENT TRENDS AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppq459-q474, April-2024, Available at : http://www.jetir.org/papers/JETIR2404H57.pdf