Groundwater is considered as cleanest and widely used source of water throughout the world. Proper management of groundwater resources requires continuous monitoring of groundwater level. To make decisions and plans for future, decision-makers need reliable tools to predict the groundwater level by developing relationships between the factors/parameters that can affect the dynamics of groundwater. With the effects of climate change such as increasing heat, higher rainfall and frequently occurring extreme weather events such as floods and draughts, the need of tools and models for studying the effects of changing climate on groundwater level fluctuations has increased. Such tools and models help decision-makers in better decision-makings. With the recent development in the field of artificial intelligence, the reliable predictions of groundwater levels are possible using machine learning models. Machine learning models are data-driven models which can produce useful results utilizing the diverse data sets and computational infrastructure. This chapter focuses on types of machine learning models such as Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Mechanism (SVM). Random Forest (RF) and Decision Trees. A detailed review of the algorithm of each model and their application in groundwater level prediction is discussed. A brief literature review of groundwater level prediction using machine learning approaches is presented for better understanding of the topic.