Climate change poses significant challenges to ecosystems globally, demanding innovative methods for environmental conservation and restoration. Restoration initiatives require significant amounts of appropriate vegetation that is both adaptive and tolerant to the specific environmental factors. This study introduces an adaptive-vegetation model designed to support ecosystem resilience in the face of climate change. Traditional restoration methods often neglect dynamic environmental conditions and ecosystem interactions, but the model employs real-time data and predictive analytics to adapt strategies to evolving climate variables. The model takes a comprehensive approach, incorporating climate projections, soil health metrics, species adaptability, and hydrological patterns to inform restoration practices. By using a mix of adaptable native species, the model promotes biodiversity. In conclusion, according to the findings of our review, paludiculture and agroforestry could be implemented as models for improving climate resilience, particularly in tropical degraded peat swamp forests. These two models could improve the environment, the economy, and social functions. Finally, improving all three of these factors improves ecological stability. This adaptive-vegetation model represents a significant shift from static, uniform restoration approaches to dynamic, data-driven strategies tailored to specific environments. The future research directions underscore the need for ongoing innovation in conservation practices to safeguard ecosystems amid unprecedented environmental changes. Future efforts will focus on enhancing the model with advanced machine learning techniques and expanding its application to additional ecological contexts.