The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between Q-slope and slope stability degree using case studies of sedimentary rock slopes from 10 regions of Iran. To this end, we have investigated 200 areas from these regions, gathered the necessary geo-technical data, have classified the slopes from a Q-slope perspective, and have estimated their stability relationships. Based on artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, Decision tree, Random-forest, Multilayer perceptron, AdaBoost, Naive Bayes and Quadratic discriminant analysis, the relationships and classifications were implemented and revised in the Python high-level programming language. According to the results of the controlled learning models, the Q-slope equation for Iran has indicated that the stability-instability class distributions are limited to two linear states. These limits refer to the B-Line (lower limit) as beta = 11.9log(10)(Q(number)) + 46.3 and the U-Line (upper limit) as beta = 17.2log(10)(Q(number)) 1+ 54.1. We present the modified Q-slope equation (beta) to correct the primary relation for sedimentary rock slopes in Iran. To this end, the beta-relation from Bar and Barton (2017) that is illustrated by Eq. (2) was modified and refined by the U-line and B-line relations as presented by Eqs. (3) and (4).