With the significant increase of wireless network nodes and traffic load in recent years, especially in the emerging internet-of-things (IoT) and vehicular networks, the design of a fast adaptive relay selection algorithm that is able to cope with a quickly changing environment became a necessity. In particular, the problem of multiple relay selection and beamforming under individual power constraints is investigated in this paper when the amplify-and-forward protocol is used to forward the data to the destination. The proposed algorithm first performs relay selection and beamforming using iterative convex optimization. The selection decisions are stored and processed before being used by a proposed multi-agent machine-learning (ML) model to imitate with high accuracy the optimal selection decision in real time with much less computational complexity. Simulation results confirm that the performance of the proposed technique is very close to the exhaustive search (ES) and to well known algorithms but with an execution time that is thousands of times shorter than traditional techniques.