A significant challenge to global health is posed by brain tumors, which affect millions worldwide. Early diagnosis as well as tumor-specific treatment improve recovery, but manual detection and quantification are time-consuming. We propose HD-MCBT, a hybrid deep learning model for multi-class brain tumor classification, segmentation, and lesion area quantification utilizing MRI images, enabling faster and accurate disease monitoring. In our research, we propose the HD-MCBT model which integrates a modified DenseNet121 with an SVM classifier and a UNet segmentation network. DenseNet121 is employed for feature extraction, followed by SVM for accurate classification. UNet is then applied to segment tumor regions, while a novel algorithm quantifies the lesion area. An MRI brain tumor dataset is utilized. During training, validation, and testing, it is intended to apply. The dataset consists of four classes such as, meningioma, pituitary, glioma, and no tumor. The proposed classification approach was compared with three transfer learning models: VGG19, MobileNetv3Large, and InceptionResNetv2. Results show that the modified DenseNet121 integrated with SVM achieved superior performance, attaining an overall accuracy of 99.79%. For segmentation, UNet attained a Dice coefficient of 93.48%. Finally, the lesion quantification algorithm provides a reliable measure of tumor area, aiding physicians in disease monitoring. Thus, the proposed HD-MCBT model demonstrates strong potential in biomedical applications, supporting improved clinical decision-making and enhancing patient outcomes.