Environmental and climate systems require collective intelligence from geographically dispersed sources because they are complex, large, and heterogeneous. Traditional machine learning solutions are challenged by data centralization, privacy, and computational cost. This chapter presents a Quantum Federated Learning (QFL) framework for use in environmental and climate sciences. The framework unifies quantum concepts like entanglement and superposition within federated aggregation to realize expressive, low-energy learning between satellite images, and decentralized sensor networks. The evaluation results indicate the superiority of the framework over baseline baselines, with 84.6% accuracy, 0.0151 mean squared error (MSE), and convergence in just 22 rounds, thus proving faster training efficiency, improved predictive accuracy, and stability against heterogeneous data distributions. Case studies demonstrate the model’s ability to predict severe weather phenomena, track pollutant dispersal, and identify forest loss, providing a scalable and privacy-sustaining solution for green environmental intelligence.