In medical image analysis, brain tumor segmentation and classification is a vital that assists in efficient detection and treatment planning. In this work, we apply the Watershed Algorithm to accomplish an assessment of performance and numerical simulation of brain tumor segmentation. The effectiveness of the algorithm in defining in terms of Precision of tumor boundaries is assessed in relation to its capacity to process medical image data, namely Magnetic Resonance Imaging (MRI) scans. We present preprocessing methods such as noise reduction and gradient-based approaches, to improve accuracy and reduce the over-segmentation problem inherent with conventional watershed methods. Parameters like Dice Coefficient, Precision, and Recall are the principle awareness of a radical overall performance overview that compares the effects with ground truth and other segmentation processes. The set of rule’s computing performance is likewise tested inside the paper, which suggests that it is probably used in actual-time scientific settings. The advanced watershed method produces competitive performance with excessive segmentation accuracy, according to the results, indicating that it is able to be beneficial in aiding in the segmentation of brain tumors.