Content-Based Image Retrieval (CBIR) is used to retrieve relevant images stored in extensive databases. It has applications in healthcare, surveillance, crime investigation, and remote sensing. This paper proposes a hybrid CBIR system that integrates deep learning-based object features using YOLOv8, texture features based on the Gray Level Co-occurrence Matrix (GLCM), and color features derived from Color Histograms into a single feature vector. The system employs the K-Nearest Neighbor (KNN) algorithm, with similarity comparison based on cosine similarity. The framework is evaluated on the Corel 1K dataset (10 categories), using precision, recall, and F1-score as performance measures. Experimental results demonstrate that the proposed approach achieves average recall, precision, and F1-score values of 80%, 77.77%, and 78.33%, respectively, outperforming several state-of-the-art CBIR systems evaluated on the same benchmark. These findings highlight the efficiency, accuracy, and computational simplicity of the system, making it suitable for large-scale, real-world image retrieval applications.