 This paper proposes a novel approach for accurately predicting the concrete compressive strength, CCS. It combines an artificial neural network, ANN, with electromagnetic field optimization, EFO, to optimize the parameters of the ANN. The EFO is then used to compare the performance of the ANN with those of three other benchmark optimizers, water cycle algorithm, WCA, sine cosine algorithm, SCA, and cuttlefish optimization algorithm, CFOA. The results indicate that the ANN EFO is more accurate than the others, while being faster as well. Additionally, a user-friendly formula is derived from the ANN EFO for easy estimation of the CCS. This article was authored by Mohammad Reza Akbarzada, Hossein Gafourian, Arsalan Anvari, and others.