Brain tumors are the most critical and life-threatening medical conditions, necessitating early and accurate detection for effective treatment planning. The main aim of the project is to investigate the application of transfer learning using state-of-the-art deep learning architectures, including VGG 16, VGG-19, ResNet-50, Inception-V3, and DenseNet-201, for accurate and efficient brain tumor detection from MRI images. These approaches are able to address the challenges such as data scarcity and computational constraints in medical imaging. The conventional manual analysis of brain image data, such as MRI scans is time-consuming and prone to subjective biases, so making automated methods highly desirable. The aforementioned methodologies leverage pre-trained models, fine-tuned to classify brain tumor images effectively. Each model was evaluated on a benchmark dataset, with preprocessing steps including normalization, augmentation, and segmentation to enhance feature extraction. Performance metrics such as accuracy, precision, recall and F1-score were employed to rigorously assess and compare the models. The results indicate that ResNet-50 demonstrate superior performance due to their deeper architectures and efficient feature extraction capabilities followed by VGG-19 and Inception-V3. DenseNet-201 exhibits notable results in terms of computational efficiency and accuracy trade-offs, while VGG-16, despite their simplicity, performs reliably in identifying tumor characteristics. This research highlights the potential of transfer learning in addressing challenges such as data scarcity and computational constraints in medical imaging tasks. By identifying the strengths and limitations of these models, the study provides a comprehensive foundation for deploying deep learning solutions in clinical settings, paving the way for improved diagnostic accuracy and efficiency in brain tumor detection.