The application of machine learning(ML) and genetic programming(GP) to the image compression domain has produced promising results in many cases. The need for compression arises due to the exorbitant size of data shared on the internet. Compression is required for text, videos, or images, which are used almost everywhere on web be it news articles, social media posts, blogs, educational platforms, medical domain, government services, and many other websites, need packets for transmission and hence compression is necessary to avoid overwhelming the network. This paper discusses some of the implementations of image compression algorithms that use techniques such as Artificial Neural Networks, Residual Learning, Fuzzy Neural Networks, Convolutional Neural Nets, Deep Learning, Genetic Algorithms. The paper also describes an implementation of Vector Quantization using GA to generate codebook which is used for Lossy image compression. All these approaches prove to be very contrasting to the standard approaches to processing images due to the highly parallel and computationally extensive nature of machine learning algorithms. Such non-linear abilities of ML and GP make it widely popular for use in multiple domains. Traditional approaches are also combined with artificially intelligent systems, leading to hybrid systems, to achieve better results.