This paper presents a geographic information system for monitoring and forecasting the spread of forest fires based on intelligent processing of aerospace data from low-orbit vehicles (LOA). The system uses convolutional neural networks (CNN) for fire detection and recurrent neural networks (RNN) for fire spread forecasting. To ensure the security of high-speed data transmission from LOA, a quantum key distribution (QKD) system is implemented, providing virtually unbreakable encryption. Experimental results demonstrate a 30% improvement in fire detection efficiency compared to traditional methods. The paper also discusses the potential costs of implementing QKD and AI, as well as the steps required for practical implementation of QKD on a large scale, taking into account factors such as the influence of the atmosphere on quantum key distribution.