This paper proposes an end-to-end framework integrating advanced cloud computing and machine learning for NDVI time series analysis and environmental change detection. The framework achieves efficient multi-source data access, quality control, and fusion analysis through modular design, and improves the accuracy and noise robustness of change identification by combining self-attention and recursive hybrid models. Multi-source data fusion and uncertainty quantification mechanisms are introduced to enhance the model’s cross-sensor adaptability and result interpretability. Deployment on a cloud platform verifies the system’s high throughput and elastic scalability in large-scale data scenarios, providing a scalable and operable technical path for ecological monitoring and agricultural management.