This study explores the application of system analysis methods to enhance space environment research, focusing on predictive modeling, data management, risk analysis, and optimization techniques. The aim is to address key challenges and improve the understanding and management of space phenomena. Methods employed include historical data analysis, predictive modeling, data standardization, ETL (Extract, Transform, Load) processes, probabilistic risk assessment, and optimization algorithms. Results indicate that predictive models effectively forecast solar flares and geomagnetic storms, aiding in space asset protection. The data integration model standardizes diverse data sources, enhancing data accessibility and analysis. Risk analysis identifies potential hazards, including technical failures and environmental risks, providing insights for mitigation strategies. Optimization techniques improve spacecraft trajectories and resource allocation, enhancing mission efficiency. The novelty of this research lies in the comprehensive integration of system analysis principles across various aspects of space environment studies, demonstrating their applicability in improving predictive accuracy, data management efficiency, risk mitigation, and resource optimization. This work lays a foundation for future research to refine predictive models, integrate real-time data, expand risk analysis, and apply collaborative optimization techniques in international space missions, ultimately advancing the safety, efficiency, and success of space exploration efforts.