This paper presents the design, implementation, and validation of the Central Asian Cotton Intelli-gence Platform (CACIP), a novel digital twin architecture developed to enhance the resilience of cot-ton-textile clusters. The platform integrates multi-source data, including Sentinel-2 satellite imagery, machine learning (ML) classification models, Industry 4.0 maturity diagnostics, and a governance scenario optimization engine, into a unified cyber-physical system. Using open-access Sentinel-2 da-ta, we demonstrate a reproducible ML experiment that achieves a cotton-field classification accuracy of 0.92 (OA) and an F1-score of 0.90. Furthermore, a resilience scoring model is introduced to formal-ize governance, technological, and social indicators into a composite index, enabling budget-constrained resource optimization. A regional case study utilizing a PostgreSQL/PostGIS spatial backend confirms measurable improvements in operational transparency, irrigation risk management, and decision-making latency. The proposed architecture operationalizes resilience as a computational-ly verifiable system and provides a scalable applied IT template for the sustainable digital transfor-mation of industrial agro-clusters.