An increase in population leads to an increase in the water demand. Technological advancements are racing to address water shortages. Recent advances in deep learning (DL), machine learning (ML), and artificial intelligence (AI) have enabled us to effectively manage water scarcity as well as optimize, model, automate, and predict water treatment processes. Additionally, computer-assisted support to complicated problems related to water chemistry, and membrane applications. In the sector of water treatment and desalination processes, various models such as Support Vector Machines (SVM), Random Forest (RF), Genetic Algorithms (GA), K-Nearest Neighbors (KNN), time series models, and Multi-Layer Perceptron (MLP) have been applied to address the issues ranging from optimization of treatment processes to predictive modeling as well as failure/fault detection. Presently, water quality forecasting lacks the much-needed precision and accuracy. Thus, a highly versatile MLP is engineered and designed to approximate any continuous functions and may solve issues that are not linearly separable. Typically, MLPs are used for pattern classification, forecasting performance, and approximation. This review paper presents automatic forecasting of water quality and desalination processes efficiency which resolves the issue of missing values from the data sets. Moreover, this paper examines a wide range of peer-reviewed, vital water-based applications using DL, ML, and AI, including membrane separation, water quality, and performance efficiency. A thorough review of MLP applications in water treatment and seawater desalination is presented here. Furthermore, the conventional modeling approaches are compared with the MLP model. It will also highlight the drawbacks that hinder the implementation of real-world water treatment and desalination processes. In conclusion, the latest developments in membrane processes, seawater desalination, and MLP-based water treatment have been summarised. © 2024
