Hospital wastewater is a significant public health concern due to the presence of various antibiotic-resistant microorganisms, pharmaceuticals, and heavy metals. These contaminants are hazardous to both the environment and human health. Microbial bioremediation is a sustainable method for the treatment of effluents, although its efficacy is dependent upon the nature of the wastewater, weather, and the geographical area. Conventional wastewater treatment is often inadequate in removing these contaminants completely. Among the different alternatives available to us, bioremediation provides a long-lasting, cost-efficient, and resilient approach. A diverse variety of microorganisms are used in this approach, such as bacteria, fungi, the integration of cyanobacterial systems, and bacteriophages, which have the potential to degrade pollutants. By integrating these microbial systems with hybrid intelligence and artificial intelligence (AI), we can optimize and monitor biotreatment units and control them in real time. This will significantly improve the pollutant removal rate. Recent developments in synthetic consortia, AI-assisted hybrid reactors, and modified microbial strains suggest that they have the potential to significantly enhance the precise data-driven treatment of wastewater. Due to these novel concepts, the development of intelligent bioremediation systems is possible. These systems will be able to function efficiently despite the varying environmental conditions and will result in a more flexible public health system, a secure environment, and hygienic hospitals.