The purpose of this article is to predict impending financial distress of the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) using a wide range of features including accrual accounting variables, cash-based accounting variables, market-based variables, corporate governance mechanisms, and macroeconomic indicators. The final sample includes 421 firms leading to 3, 670 firm-year observations. The prepared data, was then split into a train and test data set using a 70/30 ratio. In this research, various data pre-processing machine learning techniques i. e., Z-score standardization, one-hot encoding, stratified K-fold validation combined with feature engineering are applied to improve classifier performance. Stratified K-fold cross validation method, (with k = 5) was used for estimation of model prediction performance during training phase. During the training phase, hyper-parameter tuning of a model was carried out using a grid-search. Furthermore, a costsensitive meta-learning approach in conjunction with the proposed imbalance-oriented metric i. e., F1 score were used to overcome the extreme class imbalance issue. Based on the experimental results, the tuned Support Vector Machine (SVM) model achieved f1-score, MCC, recall and precision of respectively, 55%, 56%, 78% and 43% on the training set. Finally, the proposed model was tested on the hold-out test set which resulted in f1-score, MCC, recall and precision of 50%, 50%, 68% and 40%, respectively.