Landslides are natural disasters posing great risks to life, infrastructure, and the environment. Timely and accurate predictions are highly beneficial to reduce such impact. The advent of machine learning (ML) and deep learning (DL) has significantly improved the state of landslide prediction models. The review outlines the various ML and DL techniques adopted for landslide prediction and gives a brief account of methodologies, applications, benefits, and limitations. This is mainly the melding of ML and DL techniques, such as Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, for the enhancement of the predictive ability of such models. Key challenges in landslide prediction, such as data availability, model interpretability, and computational complexity, alongside future directions, will be discussed to contribute to the robustness of landslide prediction models. Finally, inferences will be drawn as to the significance of hybrid ML-DL approaches in pushing forth landslide prediction models into better accuracies and reliability (Khuc, T.D., et al., 2023) (Wu, X., et al., 2023).