Brain tumor detection using Magnetic Resonance Imaging (MRI) remains a challenging task due to tumor hetero geneity and imaging variability. This paper presents a novel hybrid Deep Convolutional Neural Network–Whale Optimization Algorithm (DCNN-WOA) framework for automated brain tumor detection and classification. The pro posed method consists of four main stages: MRI data preprocessing and augmentation, deep feature extraction using multi-layer Convolutional Neural Networks (CNN), feature selection and hyperparameter optimization via the Whale Optimization Algorithm (WOA), and final classification with comprehensive performance evaluation. By jointly opti mizing deep features and training parameters, the framework effectively reduces feature redundancy, accelerates con vergence, and enhances model generalization. Experimental results on a publicly available MRI dataset demonstrate that the DCNN-WOA model outperforms conventional CNN and state-of-the-art Deep Learning (DL) architectures, achieving an accuracy of 97.8%, sensitivity of 96.4%, specificity of 98.1%, and F1-score of 97.2%. The practical impact of this approach makes it a promising solution for real-time clinical decision-support systems in neuroimaging.