The most significant and intricate organ in the human body, the brain controls every bodily function. The number of brain tumor cases is currently rising quickly, which raises the death rate. An accurate diagnosis technique is necessary in order to treat a brain tumor and reduce mortality. Numerous techniques were proposed to treat brain tumors. In order to identify the brain tumor, an MRI picture is first obtained, pre-processed, then segmented using K-means clustering. An unsupervised technique called K-means clustering divides the random dataset into various groupings. Following segmentation, discrete Wavelet Transformations (DWT) are implemented for extracting the features, and K-Nearest Neighbor (KNN), a supervised machine learning technique that produces extremely precise predictions, is employed to classify the brain tumor. Employing the KNN Classifier, we attain 97% accuracy in comparison to the most advanced techniques.