This research aims to analyze the use of ML algorithms in the diagnosis and grouping of intracranial tumors from MRI information. A comparison was made on several algorithms such as Decision Trees (DT), Random Forest (RF), KNN (KNN), Perceptron, and the Naïve Bayes Classifier (NBC). Of all the above, RF and K-Nearest Neighbor (KNN) scored the highest accuracy rates to indicate their best diagnostic efficiencies. The approach involved using a large MRI image dataset to train and test the models. Some data preparation processes included image normalization and image segmentation to make the tumor regions conspicuous. Algorithm performance was assessed using additional measures like accuracy, precision, and recall for the technology’s potential to improve the diagnostic domain for clinicians diagnosing cerebral tumors. Among all the algorithms, RF excelled in its solid predictive performance and obtained a test accuracy of 98.72%. However, the basic and efficient feature of KNN makes it possible to implement directly in clinical environments. This study exemplified how new and complex AI methods in understanding medical images can work toward creating an avenue for early detection, thus increasing the patient’s quality of life.