The questionable concerns of water scarcity worldwide, coupled with the inefficacy of the current technologies used in wastewater treatment and the variance in the emerging influent contamination, highlights the urgency for innovative remediation techniques. The metamorphosis of wastewater systems and the combination of biological processes with artificial intelligence (AI) have rapidly developed into a potent framework for wastewater treatment in the future. This paper highlights the convergence of microbial consortia, tailored enzymatic platforms, and phycoremediation with AI-driven optimization to provide adaptive, high-performance pollutant removal techniques critically synthesizing advancements reported in recent years. According to recent studies, in this type of coupling system, the efficiency achieved reported 75%-95% reduction the chemical and biological oxygen demand (COD, BOD), addition to the elimination of antibiotics. Complementarily, through symbiotic interaction of algae and bacteria, optimized microalgal platforms achieve nutrient recovery of over 90% for total nitrogen and phosphorus. The revolution can be foreseen with the introduction of AI, such as real time effluent forecasting, deep learning guided process control enables dynamic operational optimization, resulting in energy savings of 35%-62% in aeration and pumping and a 40% reduction in operating expenses. Through unavoidable persistent challenges such as microbial community volatility, information shortage, and biosafety concerns related to changes in the strains, emerging hybrid AI-based biological treatment systems present previously unheard-of opportunities for system autonomy, robustness, and circular resource recovery. This paper outlines a route toward scalable, intelligent, and climate-resilient wastewater infrastructure by placing AI as both an analytical engine and an operational catalyst, transforming smart bioremediation from a promising idea to a key component of sustainable water management.