Background: Cancer is a leading global health concern, with early and precise detection playing a critical role in improving patient survival rates. Traditional diagnostic methods, while effective, often involve delays and human limitations. Recent advancements in artificial intelligence (AI) and machine learning have introduced new possibilities for enhancing diagnostic accuracy and efficiency, particularly through automated image analysis. Objective: This study aims to explore AI-driven techniques for cancer detection, focusing on deep learning models that analyze medical images to differentiate between malignant and benign tumors. The research evaluates various machine learning algorithms, assessing their accuracy, reliability, and potential for clinical application. Results: The findings demonstrate that AI-based diagnostic models can significantly improve the precision of cancer detection. Comparative analysis of multiple algorithms reveals that deep learning approaches, particularly convolutional neural networks (CNNs), achieve high accuracy in identifying cancerous lesions. The results suggest that AI integration in diagnostic processes can enhance early detection, reduce human error, and support medical professionals in making more informed decisions. This study reinforces the potential of AI-driven solutions in revolutionizing cancer diagnosis, paving the way for faster and more accurate detection, ultimately improving patient outcomes.