Abstract: New trends in e-commerce play a significant part in the growth of technology through the internet, and the availability of modern devices and their sophisticated functions have triggered an increase in use for many. Choosing the proper product can be challenging due to the vast array of products showcased on websites, leaving customers feeling tired. These circumstances increase the rivalry between global commercial sites, which builds the need to work proficiently to increase financial profits. Simplifying the user experience is the goal of innovative technology. Visual search is the next innovation that will decouple users from the reliance on keyboards and open a new world of opportunities. Discover how the advancements in visual search technology are influencing future internet search strategies. Human and animal brains can easily detect objects, but computers struggle with this task. The latest technology is being developed, and we are witnessing these revolutionary innovations making our lives easier.

As visual content continues to dominate digital platforms, traditional search methods struggle to deliver accurate and intuitive results. This paper explores the transformative potential of vector databases in enabling efficient and intelligent visual search solutions. By converting images into high-dimensional vectors, these databases allow for similarity-based retrieval far beyond keyword matching. Leveraging machine learning and deep neural networks, visual features are encoded and compared using vector embeddings. This new paradigm not only enhances search relevance and speed but also supports scalable and real-time applications across eCommerce, healthcare, and media. The study highlights architecture, use cases, and performance benchmarks of vector-based systems.

Keywords: Visual Search, Vector Databases, eCommerce Innovation, Machine Learning, Deep Neural Networks

| DOI: 10.17148/IARJSET.2025.12640