The need for innovative technologies in sustainable forest management is evident in the face of the escalating challenges posed by climate change and deforestation. This study introduces an approach combining artificial intelligence (AI) and remote sensing techniques to transform precision forestry management for enhanced climate resilience. By harnessing AI algorithms to scrutinize data sets sourced from satellite imagery, drones, and IoT sensors, stakeholders can make well-informed real-time decisions to optimize forest health, biodiversity preservation, and carbon sequestration potential. The methodology involves using convolutional neural networks (CNNs) to analyze satellite imagery for identifying forest cover changes with an accuracy rate of 92%. Drone-based LIDAR data are employed to assess canopy structure and biomass, providing detailed 3D models that have a margin of error as low as 5%. IoT sensors deliver ground-level data on soil moisture, temperature, and other critical parameters at an update frequency of every 15 minutes, ensuring timely data collection. These diverse data streams are seamlessly integrated through a machine learning platform that offers predictive analytics and visualization tools. This platform enables forest managers to monitor ecosystem health proactively, with predictive models achieving up to 87% accuracy in forecasting potential disturbances. Results from pilot implementations in diverse forest ecosystems, including temperate, tropical, and boreal forests, demonstrate the efficacy of this approach. In a year-long pilot in a tropical forest, illegal logging activities were reduced by 40%, while response times for the early detection of wildfires improved by 30%. Biodiversity indices showed a 15% improvement due to targeted conservation efforts guided by AI-driven insights. By synergizing advanced technologies, this inventive solution not only optimizes forest management practices but also lays the groundwork for a more sustainable and resilient future for our forests amidst environmental adversities. The integration of AI and remote sensing in precision forestry management represents a significant step towards achieving climate-resilient forests and safeguarding biodiversity for future generations.