The brain, as the core organ of the human nervous system, governs a wide range of bodily functions. These include vital processes such as breathing and regulating heart rate, as well as higher-order functions like thinking, emotional responses, and creativity. Composed of billions of neurons and glial cells, the brain is divided into distinct regions, each specializing in specific functions. Neurons in the brain communicate through an intricate system of electrical and chemical signals, facilitating information processing, memory storage, and responses to external stimuli. Investigating brain tumors is a significant aspect of medical research that aims to enhance the accuracy of diagnoses, treatment techniques, and patient outcomes. Using cutting-edge machine learning algorithms on data collected from imaging modalities like MRI and CT scans for the purpose of tumor identification, classification, and segmentation is the main focus of this research. Combining conventional machine learning techniques with convolutional neural networks (CNNs) substantially enhances the processing and interpretation of complex medical pictures. The study also focuses on developing a comprehensive dataset, employing preprocessing techniques to enhance image clarity, and applying deep learning models to accurately detect tumor locations. We use several performance indicators to evaluate the models’ efficacy, ensuring they meet clinical requirements. Our results demonstrate a significant improvement over earlier methods in brain tumor identification, with a success rate of 95.35%. This program will assist healthcare professionals, including radiologists, in making more informed decisions, which should lead to improved outcomes for patients.