This study aims to develop a forest landscape stability assessment framework that integrates structure, function, and resilience to assess forest landscape stability under different landform types on the Loess Plateau, and to propose differentiated optimization strategies. Remote sensing images and ground survey data were combined to compare the effectiveness of different machine learning models in aboveground biomass (AGB) inversion. Meanwhile, forest fragmentation and landscape multifunctionality were assessed, and a Landscape Stability Index (LSI) was proposed to quantify regional forest landscape stability. The main findings are as follows: (1) between 2000 and 2022, the degree of forest fragmentation and multifunctionality in the hilly gully region improved significantly, and the Simpson's Diversity Index (SDI) value showed an increasing trend; the plateau gully region showed a decreasing trend in the SDI value. The degree of forest fragmentation in the hilly gully region was higher and showed significant changes, while the plateau gully region was more stable, with the Interior and Dominant types dominating. (2) The eXtreme Gradient Boosting model outperformed other models in AGB estimation, with R2 = 0.81 and RMSE = 24.67 ton ha-1. (3) The LSI of the hilly gully region generally increased, especially in Yanchang, showing a significant increase in ecological stability; the LSI of the plateau gully region generally decreased, especially in Baishui, showing a trend of weakening stability. Based on the assessment results, optimization strategies for different stabilities were proposed, including the hierarchical management of fragmentation, multi-objective management to improve the SDI, and adaptive management for AGB. The forest landscape stability assessment framework proposed in this study can effectively assess the stability of forest landscapes, reveal the differences in ecological restoration in different regions, and provide new perspectives and strategies for forest landscape management and optimization in the Loess Plateau.