 This research aims to develop a new procedural binary particle swarm optimization algorithm, NPBPSO, that predicts the density and compressive strength of regional concrete mixtures based on fresh state properties obtained from several ready-mixed concrete plants in Aleppo, Syria. NPBPSO has been found to be more accurate and efficient than artificial neural network ANN and traditional binary particle swarm optimization algorithm BPSO in predicting the compressive strength of concrete with less number of iterations. NPBPSO also prevents the algorithm from falling into local solutions and reaches the optimal solution faster than BPSO, improving the accuracy of obtained compressive strength values and density by 30% and 50% respectively. This article was offered by Fatima Alsala, Mohamed Bassam Hamami, George Warda, and others.