IntroductionSelective absorption of pollutants can be attained using smart nanomaterials despite the fact that most of the conventional methods of treating water separate the sensing process from the actual remediation. This work proposes a new approach where smart nanomaterials are used in sensing water quality using artificial intelligence in a closed-loop system while estimating adsorption capacity.MethodsThe system architecture includes two interrelated modules. In Module I, contamination states are classified using seven physicochemical sensing features, namely, pH, conductivity, turbidity, total dissolved solids, temperature, responsiveness due to flow effects, and signal-related variables from sensors. The other module, Module II, estimates the performance of adsorption using 24 adsorption related descriptors such as nanomaterial dosage, contact time, concentration, chemical properties, and others. Explainable AI algorithms that were based on SHapley Additive exPlanations (SHAP) helped interpret classification results and adsorption capacity.ResultsThis approach yielded an accuracy of 90.96%, a precision of 94.39%, recall of 85.97%, an F1 score of 87.90%, and a macro-averaged AUC of 0.979. When evaluated against HydroShare’s Indian water body data set, external validation resulted in a DS-1 reference accuracy retention of 96.0% under similar sensing conditions, suggesting robust geographic generalization capabilities. Adsorption modeling returned an R-squared value of 0.986 for Pb(II), 0.993 for diclofenac, and 0.994 for ibuprofen. Closed-loop simulations cut adsorbent.DiscussionThe findings indicate that smart sensing, physics-based adsorption modeling, explainable AI, and adaptive remediation control can achieve considerable gains in the accuracy of contaminant detection, energy-efficient adsorption process, and intelligent water treatment strategy design. This approach can be viewed as a scalable, statistically supported, and interpretable framework for AI-enabled nanomaterial-based water purification systems.