Brain tumors require early and accurate detection for effective treatment. Traditional diagnostic techniques rely on manual assessment of MRI scans, which can be time-consuming and prone to errors. Artificial Intelligence (AI) and deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated high accuracy in tumor detection but often lack interpretability. This study employs Explainable AI (XAI) techniques, integrating ResNet50 with Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance transparency in brain tumor detection.The dataset used comprises MRI images labeled for tumor presence, which underwent preprocessing and augmentation for improved model performance. Our proposed approach achieved high detection accuracy while providing visual explanations, ensuring trust and usability in clinical settings. The integration of Grad-CAM enabled radiologists to interpret and validate AI-based diagnoses effectively, bridging the gap between deep learning-based automation and medical decision-making.This work contributes to AI-driven medical diagnostics by balancing accuracy with interpretability, promoting wider adoption in healthcare. Future directions include testing the model on larger, more diverse datasets and incorporating additional interpretability techniques to further enhance reliability and trustworthiness.