Crude oil pipeline failure due to corrosion processes is a global issue with detrimental effects on the environment and economy. More than 10,000 oil spills occur in the United States alone, each year. These oil spills are so prevalent that they have become the rule rather than being a 1-time incident. Many of these oil spills happen as a result of pipeline failure due to corrosion. Microbial-Induced Corrosion (MIC) accounts for 20% of the total number of pipeline corrosion incidents. Therefore, the mechanisms involved and especially in the case of microbial corrosion must be studied and elucidated.Sulfate-Reducing Bacteria (SRB) are the main culprits of MIC. The first suggested mechanism in 1930’s related high corrosion rates in buried pipelines to SRB hydrogen utilization and depolarization of the cathodic area on the metal surface. Despite its numerous flaws, it remained the most widely accepted mechanism of MIC. In 2004, a new mechanism called direct electron uptake was suggested for MIC. It related corrosivity of bacteria to direct electron uptake from metallic iron. This mechanism is not fully understood hitherto. Only a few bacteria have been isolated so far that demonstrated direct electron uptake capabilities. Most of the research has been focused on these few isolates. However, if direct-electron uptake is the main MIC mechanism, other SRB strains should possess similar capabilities. This work investigated the possibility of direct electron uptake as the main MIC mechanism for SRB D. bastinii, which has not been studied before, and D. vulgaris, an organotrophic SRB. Both are common bacteria existing in crude oil pipelines. Studies including electrochemical measurements, immersion corrosion testing, metal surface monitoring via scanning electron microscope revealed direct-electron uptake capabilities for both strains. SRB strains were tested under 18 different environmental conditions. Extremely high cathodic current densities were observed in SRB cultures confirming electron transfer from the iron surface to bacteria cells. Finally, based on the large experimental dataset provided in this work, an artificial neural network model was developed to predict MIC. This model demonstrates high correlation coefficients comparable or higher than existing models for general corrosion prediction in the literature. Revealing the predominant mechanisms of MIC along with modeling capabilities enables us to design appropriate measures to eradicate pipe failure due to MIC. Additionally the investigated direct electron uptake ability of the specific SRB strains studied can be used in microbial fuel cells for enhancing the efficiency of biocathodes.