ce provisioning, virtualization, applications, and SO on [181]. Furthermore, AI/ML is crucial for 5G networks to provide the ability to analyze the data "Tsunami" in short time, automate the network processes without being explicitly programmed, and predict the network behavior with high level of accuracy [182]. At the sequel, AI/ML mechanisms bring several benefits for 5G networks in terms of network planning and diagnostics, RAN congestion, on-demand and adaptive network configuration, service creation and orchestration, fault detection, security, handover and mobility management, user experience enhancement, and dynamic policy adjustment [183]. A Comprehensive Study on 5G Several AI/ML mechanisms have been proposed, in the literature, in order to optimize the performance of 5G networks. These AI/ML techniques can be classified into [181, 184, 185]: 1. Supervised learning: It uses a training data and comprises input and desired outputs. It includes support vector machine, Bayesian learning, regression model, K-nearest neighbor, and gradient boosting decision tree. 2. Unsupervised learning: It is based on unstructured or unlabeled data and used for data clustering, dimensionality reduction, and density estimation. It includes K-means clustering, spectral clustering, replicator neural network, and principal and independent component analysis. 3. Reinforcement learning (RL): It learns by interacting with the environment without human intervention in order to obtain the optimal policy. RL approaches include Q-learning, Markov decision process (MDP), and multi-armed bandits. 4. Deep learning (DL) or semi-supervised: It is a highly advanced unsupervised learning model that is based on neural networks. 5. Deep reinforcement learning (DRL): It is a combination of deep and reinforce- ment learning. In [181] and [185-187], the role of AI/ML mechanisms in 5G networks and research works to overcome diverse 5G challenges have been addressed. 5GPPP in [181] has investigated AI/ML methods to tackle optimization problems in 5G