As climate change accelerates, the delicate balance of our planet's ecosystems, which is vital for biodiversity and human livelihoods, are facing severe disruptions. This chapter explores the use of geospatial and Artificial Intelligence (AI) techniques to assess the impacts of climate change on these ecosystems, with a particular focus on forest and coastal mangrove ecosystems through case studies from Kaziranga National Park and the Sundarbans. By integrating satellite imagery and remote sensing data, the chapter demonstrates how machine learning (ML) models can effectively monitor ecosystem dynamics, predict changes, and support adaptive strategies. Case studies on forest aboveground biomass monitoring in Kaziranga National Park, Assam, and mangrove health monitoring in the biosphere reserve zone of the Sundarbans have been incorporated to illustrate how ML models provide valuable insights into changes in forest structure, coastal erosion, and the vulnerability of these ecosystems to climate-related stressors. The chapter also examines the broader implications of these changes for local communities and infrastructure, offering solutions for sustainable forest management and coastal protection. By combining geospatial analysis with AI approaches, a comprehensive framework for monitoring and adapting ecosystems is provided, which may help researchers and policymakers to enhance resilience and mitigate climate-induced risks.