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This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.