A number of diseases and viruses have hit communities around the globe with increased severity, aided by lifestyle changes and environmental factors. Modern technologies, such an artificial intelligence (AI), have transformed the way diseases are diagnosed, allowing accurate and quick identification that helps in classifying images of various brain tumors with an explainable artificial intelligence (XAI) and integration of convolutional neural networks (CNNs). The form of CNN with XAI in the new dimension enhances such interpretability and understanding of what these models predict and ultimately improves the sensitivity of brain tumor detection. The proposed model takes advantages of both CNNs and XAI techniques to allow healthcare providers to give more informed and accurate diagnosis decisions. Evaluation with performance metrics is comprehensive and proves multilayered CNN architecture that has an accuracy level varied between 80−86%. The interpretability introduced by XAI vastly improves the resilience of the model by predicting different brain cancers correctly for providing deep understanding into how the possibilities of cure are made.