Increasing agroforestry areas is an important step to adapt to climate change, increase food security, and have many ecological and socio-economical benefits. Proper planning and strategies are required for the assessment of land potential and selection of suitable land for the multifunctional benefits of agroforestry. Remote sensing (RS) and geographical information system (GIS) tools are widely used to identify the priority areas for agroforestry and policy-making. The multi-high resolutions of Google pro mosaicked images were used as a base map for precision, detailed analysis, and valid interpretation. To identify the farm landscape suitability areas in the Belpada block of Belangiri district, Odisha, a GIS modeling approach was used based on satellite data measurement. The post-monsoon multi-date monthly cloud-free Landsat-8 data and products of the Digital Elevation Model were used to understand the farm landscape characteristics of agroforestry. Soil wetness, slope, drainage, and Normalized Difference Vegetation Index (NDVI) were used in the preparation of landscape suitability analysis. Overall 27.8% (134.16 sq. km) of land was highly suitable, 50.0% (241.85 sq. km) of land was moderately suitable and 19.7% (94.98 sq. km) was marginally suitable and the remaining 2.5% (12.01 sq. km) of land was found unsuitable for agroforestry. Out of 116 villages, 14 villages are found with high (greater than 70%) farmland potentiality, the highest is found in the Jalia village. The moderate and highly suitable land/villages should be given preference for tree-based farming in various agroforestry arrangements. The high-resolution farm landscape potential grid maps were produced for the first time which was earlier a research gap in the past that will support micro-level agroforestry planning. There is a need for a robust synergic approach when integrated with native and multifunctional trees in potentially suitable agroforestry farmland with adequate watershed management and conservation practices enriched with indigenous knowledge that will significantly support achieving the many sustainable development goals (SDGs) up to the smallest unit (village) level.