The dyeing segment of textile manufacturing faces pressing demands to curb water, energy and chemical consumption while maintaining colour consistency and fast throughput. This paper presents a layered edge-to-cloud architecture that employs artificial-intelligence techniques to manage resources dynamically across the entire dyeing supply chain. IoT sensors on each dyeing machine stream high-frequency telemetry to an AI cloud where data are curated in a scalable lake and enriched through feature engineering. Deep-learning models forecast short-term demand, yields and process drifts, enabling proactive recipe adjustments. A digital-twin simulation engine mirrors every dye bath and tank, permitting insilico experimentation of eco-friendly formulas without risking live production. Reinforcement-learning agents, trained against the twin, generate near-optimal job schedules and set-points that minimise water and chemical footprints while respecting delivery constraints. All recommendations pass through an explainable-AI module that surfaces the key factors driving each decision to foster operator trust and regulatory compliance. A blockchain ledger records batch-level resource transactions, ensuring tamper-proof traceability for audits and sustainability certifications. The architecture integrates seamlessly with existing ERP/MES systems, closing the loop between predictive intelligence and shop-floor actuation. Proof-of-concept results from a mid-scale dye house show up to 18% water savings and 12% energy reduction, with payback achieved in under 9 months. Collectively, the proposed framework demonstrates how advanced AI and Industry 4.0 technologies can deliver both economic and environmental gains in dyeing operations.