Cancer is a grave category of illnesses in which the body's aberrant cells proliferate and spread uncontrollably. It can appear in nearly every tissue or organ and take many different forms, each with its own distinct set of symptoms and side effects. Environmental variables, lifestyle decisions, and genetic abnormalities are typically linked to the development of cancer. The varied approaches to cancer diagnosis are examined in this study, with a focus on early detection and therapeutic strategies. This literature review covers a wide range of cancer kinds, such as brain tumors, leukemia, breast, lung, and cervical cancer, and offers recommendations for creating reliable machine learning-enhanced cancer detection techniques. The research elucidates several applications, techniques, and comparative analysis in this significant subject, ranging from imaging analysis to biomarker identification. The study explores the developing methods that lead to a more precise diagnosis. The study offers insights with a thorough examination of the benefits, drawbacks, and innovations of each technique, ranging from conventional diagnostic procedures to state-of-the-art technologies. It also directs future research efforts toward the hunt for more effective personalized illness management.