Detecting lesions in colonoscopy remains a significant challenge due to the complexity of images and the limitations of labeled datasets. This study proposes a combined unsupervised learning approach for clustering colonoscopy images, which utilizes the Bag of Words (BoW) model for extracting local features, hierarchical autoencoders for dimensionality reduction, hierarchical clustering for effective data grouping, and Deep Belief Networks (DBNs) for identifying nonlinear patterns. This method significantly enhances lesion detection, especially when labeled data is scarce. Experimental results show clustering accuracy ranging from 91.97% to 100%, with a strong silhouette score above 0.80. Performance improves with larger vocabulary sizes and distance metrics such as Euclidean, Chebyshev, and Cosine. Integration of hierarchical autoencoders and DBNs enhances scalability and computational efficiency under limited labeled data conditions. The proposed method improves clustering quality without requiring large labeled datasets. This unsupervised hybrid framework is applied for colon disease detection using unlabeled data and integrates techniques from pattern recognition, deep learning, computer vision, and natural language processing, reducing reliance on labeled data while improving diagnostic accuracy.