Vector similarity search is a crucial machine learning technique used to identify similar data points within high-dimensional spaces, playing a significant role in applications like recommendation systems, image and video search, natural language processing, and clustering. This method involves representing data as vectors, computing similarity scores using various distance metrics, and employing nearest neighbor algorithms to enhance search efficiency. While vector similarity search offers improved data retrieval and pattern recognition, it faces challenges such as high-dimensional data, scalability, and the choice of distance metrics. Solutions to these challenges include techniques like dimensionality reduction, advanced indexing structures, and adaptive metrics. In computer vision, vector similarity search aids in tasks such as object detection, image retrieval, recognition, and segmentation, improving the accuracy and efficiency of visual data analysis. Overcoming the inherent challenges of vector similarity search is essential for advancing machine learning applications and enhancing user experiences.