<![CDATA[Environmental pollution from plastic waste and domestic liquid waste requires innovative, sustainable solutions. This study explores the integration of plastic-based activated carbon adsorption technology with a machine learning approach for the optimization of domestic liquid waste treatment through the Systematic Literature Review method. A literature search was conducted on the Scopus, Web of Science, ScienceDirect, and Google Scholar databases for the period 2021-2025, resulting in 10 high-quality articles that were comprehensively analyzed. The synthesis results showed that activated carbon from PET, PVC, and chemical activated plastic waste produced a material with a BET surface area of 800-1500 m²/g and an adsorption capacity of 1086 mg/g comparable to commercial products. Specific surface area parameters and initial concentrations of pollutants were the most dominant factors with a combined contribution of more than 43% to the removal efficiency. Machine learning algorithms such as Extreme Gradient Boosting, Gaussian Process Regression, and multimodal deep learning achieve very high prediction accuracy with R² up to 0.99 and RMSE below 5 mg/g. Optimization based on metaheuristic algorithms increases adsorption capacity by up to 15.40% with material reusability reaching 70-90% after five cycles. Competitive production costs ($13.75/kg) and low carbon footprint (5.92 kg CO₂/kg) make this technology a sustainable solution that supports the circular economy. The framework developed provides a scientific foundation for the implementation of an efficient and environmentally friendly liquid waste treatment system]]>
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