This article presents an innovative approach to estimate the unconfined compressive strength (UCS) of peat-enhanced bricks using a hybrid intelligent system (HIS) resulting from integration of support vector regression (SVR) and Bat meta-heuristic algorithm (hereafter, Bat-SVR). First, peat-enhanced brick specimens were prepared for various compositions of cement, sand, and peat (odd-valued array of peat inclusion in the range of 0-29% from the total specimens' weight). Further, the experimental works were carried out to obtain the UCS of specimens in different curing period. Finally, HIS model was used to predict the UCS of cement-peat-soil mixture. Basically, we used a newly-developed Bat algorithm for tuning the SVR parameters, because the accuracy of SVR estimation highly relies on these parameters. Results from the experimental study were used to train and estimate the UCS of peat-enhanced bricks. In addition, we compared the accuracy of the developed HIS model to other conventional soft computing techniques (i.e., ANFIS and neural network). It was found that the proposed approach outperforms the other conventional prediction models and better estimates the UCS of peat-enhanced bricks. (C) 2015 Elsevier B.V. All rights reserved.