One of the most important problems in clinical neuroscience and medical imaging is still the identification and diagnosis of brain tumors. Improving patient outcomes and facilitating prompt medical intervention depend on the early and precise detection of brain tumors. Despite their effectiveness, traditional diagnostic methods often depend significantly on the knowledge of radiologists and the manual interpretation of medical pictures, which may cause errors or delays in diagnosis. The field of clinical diagnostics is changing dramatically with the introduction of artificial intelligence (AI), especially sophisticated machine learning (ML) techniques. With the goal of improving diagnostic precision, effectiveness, and repeatability, this work offers a thorough machine learning-driven decision-making framework for the clinical diagnosis of brain tumors. The models were trained and validated on a variety of annotated brain tumor MRI datasets. Gliomas, meningiomas, and pituitary tumors are among the several tumor forms included in the dataset. Standard performance criteria, including as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), were used to assess the suggested methodology. With an accuracy of 97.4% and an AUC of 0.98, the ensemble technique combining CNN and SVM fared better than the other models under evaluation, proving its resilience and dependability in real-time clinical scenarios. The enormous potential of machine learning to support clinical decision-making procedures for brain tumor diagnosis is shown by this study. It not only speeds up diagnosis but also reduces human error, opening the door to more reliable and expandable medical solutions. However, in order to handle ethical concerns, data privacy problems, and the need for thorough validation in various clinical contexts, the integration of AI into clinical processes requires ongoing cooperation between data scientists, medical professionals, and regulatory agencies.