<p>The expansion of the textile and dyeing industry contaminates freshwater resources with toxic compounds. This pollution threatens the natural environment's growth by introducing harmful coloring agents. Approximately 15% of synthetic industrial wastes, predominantly from textiles and pharmaceuticals, inadvertently enter into the main water sources. Nanomaterials, especially nanoparticles, can be developed through physical, chemical, and biological processes, that can help in degrading or eliminating these pollutants. Zirconium nanomaterials are highly effective in water purification and dye degradation due to their ability to generate reactive oxygen species (ROS) and high surface area. They also enhance photodegradation reactions through their semiconductor properties and high catalytic efficiency. Integrating artificial intelligence (AI) and machine learning (ML) has significantly improved the synthesis and optimization of zirconium nanoparticles. AI-driven models analyze extensive datasets to identify optimal synthesis parameters, while machine learning algorithms predict how variations in nanoparticle design affect performance against microbial contaminants and dye degradation applications. For determining the electronic structure of molecules and materials, DFT offers a trustworthy technique. The stability and reactivity of nanomaterials are determined by electron correlations, which are effectively considered. Commonly used software for these calculations includes Gaussian 09, utilizing pseudopotentials such as the 6-31G + (d, p) basis set which provides accurate treatment of electron interactions. The findings underscore the importance of AI, computational modeling, and nanotechnology in advancing the potential applications of zirconium nanomaterials for environmental remediation.</p> Graphical Abstract <p></p>