DOI: https://doi.org/10.36347/sjet.2025.v13i02.001

Pages: 69-72

Cancer is a complex diseases that has diversity in terms of its origins, genetic variations and has been categorized as numerous distinct sub types. Early cancer diagnosis and prognosis are essential for patient care and cancer research because they allow for more individualized treatment and better treatment results. So the Machine learning methods invoke model progression and treatment of cancer condition. The machine learning tools are able to extract significant information from intricate data and is indispensable for enhancing decision making and addressing challenges in numerous sectors. Various of techniques, including Bayesian Networks (BNs), Decision Trees, Support Vector Machines (SVMs) and Artificial neural networks (ANNs) are used for the development of predictive model. Therefore, enhancing existing A.I. and M.L. technologies and fostering new advancements are crucial for patient benefit. This literature review delves into the application of A.I. and M.L. algorithms in cancer prognosis, discussing their present use, constraints, and prospects for the future. Clearly our objective is to demonstrate that employing machine learning can advance our understanding of cancer progression, ultimately leading to improved and more accurate decision making in realm of cancer research and treatment.