Prediction of groundwater levels using machine learning techniques has gained substantial attention over the past few decades. Several researchers have reported the advances in this field and provide clear understanding of the state-of-the-art machine learning models implemented for GWL modelling. However, not many studies discuss the application of these results, which is of utmost importance to the practicing engineers and decision-makers for efficient planning and management of water resources infrastructure. The first part of this chapter provides a detailed review of the existing machine learning models in the domain of groundwater hydrology. This review provides a summary of all types of machine learning models developed for estimating groundwater levels over the past decade. The review also discusses the challenges associated with the use of machine learning in groundwater levels estimation. In addition, several studies from the recent past indicate the dominance of Ensemble Machine Learning in managing the sustainability of groundwater across the globe. So, the ability of ensemble machine learning models in estimating the groundwater level is discussed in the chapter. Furthermore, the chapter provides recommendations to enhance the knowledge in this domain and directions for the possible future research to improve the accuracy of the machine learning models in estimating groundwater levels.