The effectiveness of bioflocculants in wastewater treatment can be influenced by various factors, including wastewater composition, pH, temperature, and the dosage of flocculants. Despite these variables, bioflocculants offer a sustainable and environmentally friendly alternative. The integration of machine learning (ML) techniques into bioflocculant-based treatment processes has the potential to enhance performance, efficiency, and cost-effectiveness. ML algorithms can analyze extensive datasets from wastewater treatment plants, which include information on influent characteristics, treatment parameters, and effluent quality, to identify patterns and correlations. Utilizing this data, machine learning models can predict the optimal dosages of bioflocculants and the conditions necessary for treating wastewater with diverse compositions, thereby maximizing flocculation efficiency while minimizing chemical usage and operational costs. Furthermore, ML can facilitate real-time monitoring and management of bioflocculant performance within treatment systems. Sensors and Internet of Things (IoT) devices can measure critical parameters such as turbidity, suspended solids, and organic content. Subsequently, ML algorithms can process this information to provide insights and adjust treatment protocols accordingly. Additionally, machine learning can support the development of advanced bioflocculant formulations with improved properties and stability. By examining the molecular structures and physicochemical characteristics of bioflocculant molecules, ML algorithms can determine the most effective formulations tailored to specific types of wastewater and treatment goals, thereby expediting the discovery and optimization process.