Cancer detection has witnessed a paradigm shift with the advent of artificial intelligence (AI) and machine learning (ML), revolutionizing traditional diagnostic approaches. This research explores advanced computational techniques, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Long Short-Term Memory (LSTM) networks, XGBoost, and 3D-CNNs, applied across a spectrum of cancers such as lung, liver, ovarian, brain, and thyroid. These methods leverage medical imaging and cytology data to enhance accuracy, sensitivity, and early-stage identification, addressing limitations in conventional methods. By utilizing deep learning architectures and hybrid models, the study highlights their potential in improving diagnostic precision, reducing variability, and facilitating personalized treatment strategies. However, challenges like data heterogeneity, interpretability, and clinical integration persist. The paper underscores the need for explainable AI, robust validation frameworks, and interdisciplinary collaborations to overcome these barriers. Concluding with a forward-looking perspective, it emphasizes the transformative role of AI/ML in oncology, advocating for continued innovation and global cooperation to realize the vision of accessible, accurate, and proactive cancer diagnostics.