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Rangelands and pastoral systems play vital roles in biodiversity conservation, carbon sequestration, and supporting livelihoods. However, their vast and heterogeneous landscapes pose significant challenges for assessing and managing ecosystem services (ES). This study aimed to develop a comprehensive framework to quantify the impacts of regenerative agriculture (RA) practices within these ecosystems, focusing specifically on Western Australia's rangelands. These practices offer a sustainable approach to enhancing ecosystem resilience, yet their effects on ES remain underexplored. The framework integrates the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, remote sensing technologies such as high-resolution satellite imagery, and machine learning (ML) techniques. These methodologies systematically map and assess key ecosystem services across different spatial scales, including provisioning (e.g., net primary productivity (NPP)), regulating (e.g., water dynamics), cultural (e.g., habitat quality), and supporting services (e.g., soil health). Using field and climate data from 2000 to 2023, along with satellite indices (NDVI, LAI), were used to calculate Photosynthetically Active Radiation (PAR) and Fraction of PAR Absorbed (FPAR), which are crucial for estimating Net Primary Productivity (NPP). Advanced ML algorithms, including Random Forest and Neural Networks, are employed to predict key ecosystem services. The framework supports the simulation, evaluation, and optimization of regenerative agriculture practices, thereby enhancing ecosystem resilience and promoting sustainable rangeland resource management. Forecasting ecosystem values in monetary terms, especially under regenerative agriculture scenarios, equips policymakers with tools t o assess policy outcomes and advance sustainable land management in rangelands and pastoral systems. Despite its capabilities, persistent challenges such as data availability, the nuanced and context-specific nature of regenerative agriculture scenarios, and ethical considerations —such as community involvement in data collection —are critical for successfully implementing the framework.