Forest degradation significantly impacts biodiversity, carbon storage, water regulation, and forest-dependent livelihoods, necessitating effective restoration efforts. Identifying optimal sites for forest landscape restoration is critical for sustainable forest management. This study employed a GIS-based Multi-Criteria Decision Analysis (MCDA) using the Analytical Hierarchy Process (AHP) to assess forest landscape restoration suitability in Ethiopia's Libokemkem District. Sampling techniques and data collection instruments involved acquiring high-resolution geospatial data from diverse sources, including ALOS PALSAR DEM (12.5m), PERSIANN-CCS rainfall (4km), Sentinel-2 LULC (10m), FAO soil maps, and OpenStreetMap for infrastructure. Ground truth data from Google Earth validated the LULC map, achieving an 85% overall accuracy and a Kappa coefficient of 74%. Expert judgment, formalized through AHP pairwise comparisons, determined factor weights. The Consistency Ratio (CR) of 0.056 (5.6%) confirmed high judgment reliability. LULC (26%), Slope (20%), Distance from Forest Patches (15%), and Proximity to River (13%) emerged as the most influential factors. Conversely, Distance from Roads and Settlements (both 3%) had minimal influence. The Weighted Overlay Analysis revealed that the majority of the district (61.29%) is Moderately Suitable (S2) for restoration, covering 845.93 km2. Marginally Suitable (S1) areas constitute 38.10% (525.84 km2), while Highly Suitable (S3) sites are scarce, accounting for only 0.62% (8.49 km2). Negligible areas were classified as Not Suitable (N). This geospatial framework provides a robust, data-driven foundation for strategically prioritizing and implementing targeted forest restoration initiatives across suitable zones. Its application is crucial for enhancing ecological resilience and fostering sustainable socio-economic benefits in the region.