Manual estimation of compressive strength of concrete (CSC) is a time consuming and difficult task. It has been an active area of research in the domain of manufacturing engineering. Soft computing techniques are found better to statistical methods applied for prediction of CSC. However, sophisticated prediction models are still missing and need to be explored. Random vector functional link network (RVFLN) significantly reduces the time complexity by assigning input layer weights and bias randomly without further modification. Only, output layer weights are calculated iteratively by gradient methods or non-iteratively by closed form solution like least square methods. It is an efficient algorithm with low time complexity and is able to handle complex domain problems without compromising on accuracy. Motivated from characteristics of RVFLN, in this work we develop a RVFLN-based forecast for estimating compressive strength of concrete cement. A publicly available dataset from UCI repository is used to develop and access the performance of the model. For comparative analysis, few other models such as FLANN, MLP, RBFNN, MLR, and ARIMA are also developed and used for the forecasting considering samples with curing ages at 14, 28, 56, and 91 days. All the models are evaluated in terms of MAPE, ARV, U of Theil’s statistics (UT), NMSE, and execution time. Outcomes of the comparative studies and statistical significance tests are in favor of RVFLN-based forecasting.