Content-Based Image Retrieval (CBIR) is a vital area of research in computer vision, focusing on retrieving relevant images from large datasets based on their content. With the integration of machine learning and Natural Language Processing (NLP) techniques, CBIR systems have evolved to understand semantic content, improving the precision and relevance of image retrieval. This review paper explores the advancements in CBIR using machine learning and NLP, focusing on how NLP techniques can be leveraged to enhance image understanding and retrieval processes. The reviewed studies emphasize the fusion of visual and textual information, deep learning models, and attention mechanisms to bridge the gap between image content and user queries. The paper identifies key challenges in scalability, real-time retrieval, and semantic understanding, and discusses future opportunities for integrating more robust NLP methods, including transformer-based models and multimodal learning frameworks. The goal is to provide a comprehensive understanding of current developments and propose avenues for future research in CBIR with machine learning and NLP techniques.