A gene is a fundamental unit of heredity that is composed of a specific sequence of nucleotides within a DNA molecule. It contains the instructions for building and maintaining the structures and functions of living organisms. Gene function prediction involves inferring the biological function of a gene based on various types of data, such as sequence information, gene expression profiles, protein-protein interactions, and evolutionary conservation. Machine learning has emerged as a powerful tool for gene function prediction due to its ability to analyze large and complex datasets, identify patterns, and make predictions. Machine Learning algorithms, including supervised, unsupervised, and semi-supervised techniques, are applied to gene function prediction tasks. Some algorithms commonly used for gene function prediction include decision trees, random forests, support vector machines, neural networks, and deep learning architectures like convolutional neural networks and recurrent neural networks. ML-based gene function prediction has numerous applications, including prioritizing candidate genes for functional validation, identifying drug targets, understanding disease mechanisms, and predicting gene-disease associations. This book presents the complex subject of gene function prediction in the most comprehensible and easy to understand language. The objective of this book is to give a general view of the different areas of machine learning and its applications in gene function prediction. This book is a vital tool for all researching or studying gene function prediction as it gives incredible insights into emerging trends and concepts.