Natural ecosystems provide vital resources for wildlife as well as human wellness through many ecosystem services (ES). ES are greatly impacted by changes in land use/land cover (LU/LC), which are influenced by both human and natural processes. This study focused on examining the micro-level variations in ES due to LU/LC change in the selected sub-districts of the Central National Capital Region (CNCR) between 1991 and 2021. The LU/LC classification was made using a hybrid machine learning (ML) model integrating support vector machine (SVM), artificial neural network (ANN), and random forest (RF) algorithms. By using multi-temporal LU/LC maps, ES were quantified for different LU/LC classes using the global coefficient of 2014. The evaluation of the LU/LC change’s impact on ES valuation was conducted through elasticity approaches. The result shows a substantial increase of 181.49% in the total built-up area from 1991 to 2021, while the total cropland experienced a significant decline of 33.09% in the study area, including a faster reduction in cropland. During 1991–2001, the ecosystem services values (ESVs) have witnessed an increase of 11.85% in CNCR. The maximum increase in ESVs has been noted from the Gurgaon, Faridabad, and Ghaziabad sub-districts. Moreover, among all LU/LC classes, cropland exhibited the maximum decrease in ESVs during the same period in Gurgaon, Gautama Buddha Nagar, and Najafgarh. The change in ES in urban areas affects air quality, the urban heat island effect, water infiltration, and recreational opportunities. The results can help planners and researchers to draw up long-term management plans for local ecosystems, considering the consequences of changes in LU/LC on the ES.