The precise interpretation of brain tumor medical images through valid clinical diagnosis methods remains essential. The research implements a unified structure incorporating Deep Neural Networks with Explainable Artificial Intelligence features to improve brain tumor detection and display model reasoning methods. This research implements a unified structure that incorporates Deep Neural Networks (DNN) with Explainable Artificial Intelligence (XAI) features to improve brain tumor detection and display model reasoning methods, with a specific focus on identifying the dedicated location of the tumor. A diverse set of brain MRI scans enabled researchers to create a DNN model which received LIME-based XAI enhancements to produce comprehensible model decisions. The developed DNN architecture showcases improved training proficiency for distinguishing between glioma, meningioma and pituitary tumor and non-tumor tissues and achieved 81% confirmation accuracy. Through LIME we obtain interpretability features which our method uses to supply visual explanations about MRI image features that aid healthcare experts during prediction tasks. A feature of transparent functionality enables the model to gain clinical use capability which enhances medical staff acceptance of AI-based diagnosis systems. The research primarily develops transparency along with model performance optimization throughout its architectural design. This model stands apart from field models since it provides both accurate predictions and interpretable information for every prediction result. Research evidence shows that deep learning systems connected with explainable AI technologies deliver more precise predictions as well as model transparency which leads to developing reliable applications for medical diagnosis.