Natural Resource Rich Regions (NRRRs) are ecologically and economically vital regions that support the livelihood of people through the sustained ecosystem process involving interaction among biotic and abiotic elements. Identifying NRRRs, considering spatially ecological, geo-climatic, biological, and social dimensions, would help in conservation planning and prudent management of natural resources as per the Biodiversity Act 2002, Government of India. Changes in the landscape structure would lead to alterations in the composition and health of these regions with irreversible changes in the ecosystem process, impacting the sustenance of natural resources. Landscape dynamics is assessed by classifying temporal remote sensing data using the supervised machine learning (ML) technique - Random Forest (R.F.) algorithm. Additionally, predicting likely land use changes in ecologically fragile areas would help formulate appropriate location-specific mitigation measures. Modeling likely land uses through the simulation of long-term spatial variations of complex patterns has been done through the CA-Markov model. Prioritization of NRRRs at disaggregated levels highlights that 12% of the total geographical area of the district is under NRRR 1 and NRRR 2, 54% of the total geographical area under NRRR 3, and the rest of the region under NRRR 4. The current study emphasizes the need for robust decision support systems to aid in effective policy formulation for conserving and restoring natural resources.