Computational chemistry is becoming a key tool for comprehending and creating new treatment materials due to the growing need for sustainable and effective wastewater treatment methods. This study thoroughly examines the molecular modeling, material design techniques, and mechanistic insights that support the creation of novel wastewater treatment solutions. Researchers can decipher the molecule-level adsorption, degradation, and catalytic processes of pollutants by utilizing density functional theory (DFT), molecular dynamics (MD) simulations, and quantum chemistry computations. In addition to making, it easier to identify active sites and reaction pathways, these insights also make it possible to rationally design functional materials with improved specificity and efficiency, such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), graphene-based composites, and photocatalysts. The optimization of structural and electrical characteristics is made possible by molecular modeling, which also helps forecast the physicochemical interactions between pollutants and treatment materials. Furthermore, machine learning integration and computational screening are becoming effective strategies for speeding up the search for new therapeutic ingredients. Recent developments in computational methods for wastewater treatment are summarized in this study, emphasizing the cooperation between theoretical forecasts and experimental confirmations. The focus is on how computational insights drive process optimization and material innovation in the removal of new pollutants, heavy metals, dyes, and medicines. To create next-generation materials for water purification, the paper ends by describing upcoming potential and difficulties in combining data-driven design, multiscale simulations, and green chemistry principles. This integrated computational method has the potential to transform wastewater treatment technology and advance public health and environmental sustainability.