atmospheric pollutant emissions, and solid waste handling has also stimulated a search for improved techniques and the development of innovative treatment methods [4; 5; 6]. The treatment of effluents containing organic pollutants has long been a key concern in the petrochemical industry and has gained increasing attention in sectors such as paper, textile, rubber, leather, paints, cosmetics, and food processing. Synthetic pigments and dyes used in these industries are major pollutants. Common treatment methods include membrane filtration, ion exchange, chemical precipitation, biodegradation, advanced oxidative processes, adsorption, and electrochemical oxidation. Among these, adsorption remains one of the most widely employed techniques due to its efficiency in pollutant removal [7]. The ability to construct equilibrium isotherms and kinetic models allows for the predictive optimization of operational conditions. Furthermore, adsorption offers greater flexibility, rapid adjustments, high efficiency, and operational simplicity. Various adsorbents, including activated alumina, activated carbon (AC), silica gel, and zeolites, have been applied for environmental applications, with AC being one of the most widely used adsorbents due to its high efficiency. However, the high production and regeneration costs of AC for large-scale applications have prompted the exploration of alternative materials [8]. Agricultural residues, e.g., cornstalks [9], rice husks [10] okra [2], cherry tree branches [11], sawdust [12]), chitosan [13], municipal solid waste [14], clay minerals [8], metal oxides [15], and polymers [16], among others, been investigated as potential substitutes. While these renewable materials show promise, many of them require extensive studies to fully understand the physicochemical interactions between adsorbent and adsorbate governing adsorption processes in various conditions. To establish the relationship between the dependent and independent variables, it is necessary to carry out batch experiments, changing one variable at a time while keeping the others constant. As a result, a wide range of laboratory experiments are needed to ensure application beyond the bench scale, which often requires significant time, energy, effort, and costs. Modeling is a procedure that helps overcome this issue [15; 17]. In recent years, the use of data analysis tools using artificial intelligence, as well as using machine learning, has piqued significant interest in the academic community [17]. Artificial intelligence (AI) models applied to adsorption make it possible to correlate and predict kinetics, isotherms, and breakthrough curves for a wide range of adsorbents and adsorbates from a water treatment perspective [17]. For some water-treatment-related fields, a growing trend of research available on _Google Scholar_ has been observed, and this has been focused on the use of AI for prediction, modeling, control, automation, and operational performance improvement [18]. However, applications of AI tools in environmental remediation, in general, have still been limited due to their relatively uncommon use in water treatment systems [1]. Nonetheless, many researchers have identified the potential application and uses of these tools in areas such as biology, chemistry, pharmacy, ecology, engineering, biotechnology, food industry, and, more recently, in environmental treatment [19]. Artificial intelligence emerged in the mid-1950s and has since become a powerful tool in engineering, addressing complex real-world problems that traditional methods cannot solve. One notable development has been that of artificial neural networks (ANNs), which constitute a subset of machine learning (ML) that was introduced to the field of computational science in the mid-1950s. Since then, a significant number of practical and powerful tools have been employed in the field of engineering to overcome complex problems and solve intricate real-world tasks for which classical and conventional approaches are ineffective or unfeasible [18].