In this research paper, author explore the effectiveness of artificial intelligence (AI) algorithms in early cancer detection programs, specifically targeting breast and lung cancer. Recognizing early detection as vital for improving patient outcomes and survival rates, author conduct a systematic review of literature and case studies published between 2015 and 2024. The findings reveal that AI tools significantly enhance detection accuracy, reduce false positives and negatives, and streamline diagnostic workflows. He analysis highlights the advancements in mammography and low-dose computed tomography (LDCT) facilitated by AI. In breast cancer detection, AI algorithms have demonstrated the ability to identify subtle imaging patterns, thereby minimizing unnecessary biopsies and improving sensitivity for early-stage cancers. Noteworthy case studies illustrate AI's superior performance compared to traditional radiologists, leading to better diagnostic accuracy. Similarly, in lung cancer detection, AI enhances the effectiveness of LDCT by accurately distinguishing malignant nodules while reducing the need for invasive follow-up procedures. The integration of AI-driven detection programs has proven to facilitate earlier interventions, thus increasing survival rates and relieving the burden of misdiagnoses. Despite these promising advancements, he address critical challenges including data bias, the interpretability of AI models, and privacy concerns. Author emphasize the importance of continued research to optimize AI technologies for diverse populations. Ultimately, this paper underscores the transformative potential of AI in oncology, advocating for its ethical integration into routine diagnostic practices to achieve improved patient outcomes.