In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R² value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management.

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR), and support vector machines (SVM)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers.