Brain tumors present a formidable healthcare challenge worldwide, especially in India, where around 50,000 new cases emerge annually. This study proposes an innovative method utilizing Machine Learning (ML) and Deep Learning (DL), particularly Convolutional Neural Networks (CNN), to swiftly and accurately detect brain tumors from MRI images. By leveraging ML and DL, the research aims to transform patient care by aiding radiologists in prompt decision making and ensuring timely treatment. Additionally, it offers insights into the performance comparison of ML and DL models, guiding future advancements in automated brain tumor detection systems. The results showcase a remarkable 92.86% accuracy rate, with detailed analysis revealing high precision and recall metrics. This comprehensive evaluation underscores the model’s balanced performance, marking significant progress in the development of dependable brain tumor detection systems. Integration of ML and DL not only enhances diagnostic accuracy but also opens avenues for personalized treatment strategies, ultimately improving healthcare outcomes and saving lives.