<p>The growing global demand for cleaner fuels has intensified efforts to reduce sulfur emissions due to their severe environmental and health consequences, including acid rain formation and particulate pollution. Conventional hydrodesulfurization (HDS) technologies, while effective, are energy-intensive, costly, and often ineffective at removing refractory organosulfur compounds under mild operating conditions. These limitations highlight the need for sustainable and economically viable alternatives. Biodesulfurization (BDS), a microbial process capable of selectively removing sulfur from complex hydrocarbons under ambient conditions, has emerged as a promising complementary approach. Among various biocatalysts, <i>Desulfovibrio desulfuricans</i> an anaerobic sulfate-reducing bacterium demonstrates exceptional sulfur-reducing efficiency, tolerance to harsh environments, and the ability to process diverse sulfur-containing compounds. Its metabolic versatility makes it particularly suitable for integration into engineered systems such as bioscrubbers. Within the circular economy paradigm, which promotes waste minimization and resource recovery, BDS aligns well by converting waste sulfur into valuable elemental or reusable products, thereby enabling closed-loop sulfur management. This integration not only achieves efficient pollutant removal but also supports resource valorization and industrial sustainability. Although the application of <i>D. desulfuricans</i> in BDS has primarily been explored through computational and laboratory-scale models, emerging research underscores the potential of artificial intelligence (AI) and machine learning (ML) for enhancing process monitoring, optimization, and scalability. Bridging microbial biotechnology with computational intelligence offers a transformative pathway toward efficient, cost-effective, and environmentally responsible sulfur recovery systems.</p> Graphical abstract <p></p>