Microbial corrosion, a direct consequence of uncontrolled microbial activity, is one of the leading causes of pipeline failures in oil and gas industries, especially in non-scrapable pipelines such as flowlines and trunklines. With approximately 20% of corrosion challenges attributed to microbial corrosion, recent shifts in the industry – aging oilfield and increased water injection – are intensifying this problem, leading to higher water cuts in crude oil and reduced pipeline flow rates, which create favorable conditions for microbial corrosion. To address this, the present study implements a mechanistic model, calibrated and validated against experimental pit depth observations and SEM/EDS analyses. This implemented model provides a reliable predictive tool for assessing microbial-induced corrosion, aiding in optimal pipeline design and timely interventions. While many pipelines turn to chemical and physical biofilm inhibitors, leveraging such a predictive model paves the way for more proactive and cost-effective asset management, ensuring enhanced operational continuity and reduced maintenance expenditures.