Images have become an important part of any website. According to HTTP Archive, images make about 54% of the total webpage's weight. The Websites that load faster have better user engagement. So, if we can reduce the image size without compromising its visual quality, then it will have positive impact on page load time. Image Compression is used to minimize the amount of memory needed to represent an image. In this project, K-means Clustering algorithm has been used for the image compression. It is an unsupervised algorithm that automatically detects the clusters in the image data and groups each cluster of data together. The clusters are iterated over to reduce the redundancy present in the image to obtain the compressed image. In this approach the size of the compressed image is M X N X 3, therefore the image size is reduced maintaining the visual similarity between the original and the compressed image. The images were uploaded in a static website and the website was hosted locally, the load time of the website was compared and was found that the load time of the website with the compressed images was reduced in comparison to the load time of the website with the original images. Hence, it was concluded that the load time of the website is effected with the size of the image.