In today's competitive world, corporate executives are looking for financial accountability and audit firms are looking to implement strategies to increase their efficiency; Now, in order to achieve this goal, the factors influencing the efficiency should be identified and attempted to eliminate or reduce the deterrent factors. Thus, an auditor needs a tool that informs the possibility of risk and distortion. The technology advances and applying in the various sciences caused auditors to use a new data analysis techniques to increase the efficiency of audit procedures. The purpose of this study is to predict the type of independent auditor's opinion in Tehran Stock Exchange using heuristic algorithms and logistic regression including 980 observation during 2009-2015. In this study, 33 financial and non-financial variables such as indices of liquidity, asset management, profitability, debt management, market value, growth and size of the firm, employees’ productivity, corporate governance, bankruptcy, corporate performance and other influential factors (company life and industry type) are used to predict the auditor's report. Comparison of the performance of the method is shown the neural network optimized with the colonial competition algorithm, with 94.98% prediction accuracy in comparison to other methods, has the best performance in predicting the type of the independent auditor's report. Also, the change in the independent auditor's report type, the previous year audit report, investment efficiency, current ratio, debt ratio, company loss, price to revenue and net profit ratio have the greatest impact on predicting the type of the independent auditor's report.