Brain cancer remains a life-threatening condition with a very high global mortality rates, affecting the human being of all ages and ethnic background across diverse populations. The rate of survival can be enhanced by detecting the brain cancer early and with the accurate assessment of the severity of brain cancer severity, the effective treatment can be avail to the patient, which can be a great help to improve survival rates. This research demonstrates the application of machine learning techniques, in specific Deep Convolutional Neural Networks (DCNN), to classify brain cancer classification cases into low & high-risk groups, which enable timely and precise decision-making in the field of biomedical sector. The suggested DCNN model is designed to process MRI (Magnetic Resonance Imaging) data and takes the advantages of MRI’s advanced feature identification and matching capabilities. By analyzing key feature points in training and test images, the model delivers highly accurate classification outcomes. The dataset utilized in this research was taken from the Kaggle machine learning repository, ensuring a comprehensive and reliable foundation for experimental analysis. Extensive evaluation reveals that the DCNN model outperforms traditional methods such as K-Nearest Neighbors (KNN), Random forest (RF), Support Vector Machines (SVM), and Logic Regression (LR). It achieved higher accuracy in risk-level classification and also demonstrates superior computational efficiency, making it a robust and practical solution for real-world medical applications. This research emphasized the potential of DCNN-based methodologies in advancing cancer diagnostics. By providing a faster, more accurate, and scalable approach, the proposed model contributes to improving early detection and risk assessment of brain cancer, ultimately enhancing patient outcomes and advancing medical research.