Osteoarthritis is an infirmity marked by gradual degradation and loss of specialized connective tissue called articular cartilage, with symptoms that can be manageable but irreversible. Our methodology presents a holistic approach to locating and categorizing Knee Osteoarthritis (KOA) in the X-ray images, integrating advanced feature extraction, segmentation, and deep learning techniques. 5778 KOA-related images taken from the standard Osteoarthritis Initiative (OAI) database are subjected to preprocessing using Extreme Learning Machine AutoEncoder (ELM-AE) denoising and further resized, then enhanced to address noise and variability. OTSU's thresholding and morphological operations are applied in segmentation to isolate knee joint regions. Feature extraction encompasses region-based, Zernike, Haralick texture, and wavelet features, providing insights into knee OA characteristics. The classification process utilizes a Deep Convolutional Neural Network (DCNN) architecture, featuring convolutional and max-pooling layers for feature extraction, fully connected layers for decision making, and activation functions. Z-score and batch normalization are applied to enhance stability and convergence. Training and optimization use Adam optimization, with shallow fine-tuning enhancing efficiency. The novelty lies in the enhanced classification model, which encompasses multiple stages for preprocessing, segmentation, feature extraction, and classification, each refining final classification outcomes. This offers an innovative approach to KOA detection, leveraging advanced techniques, and has attained an accuracy of 96.31% for testing and 95.80% for validation. It holds promise for aiding medical practitioners in early KOA diagnosis and management.