Typical water treatment processes are essential for mitigating the risk of microplastic contamination in drinking water. The integration of experiments and machine learning offers a promising avenue to elucidate microplastic removal behavior, yet relevant studies are scarce. To address this gap, this study combined experimental and artificial neural network (ANN) modeling to explore the removal behavior and mechanisms of five neglected microplastics in typical coagulation-ultrafiltration processes. Experimental results demonstrated that coagulation achieved an optimal removal rate of 37.0-56.0 % for the five microplastics, and subsequent ultrafiltration almost completely removed all residual microplastics. Five ANN models were constructed and optimized by adjusting activation functions and employing batch normalization, accurately predicting microplastic removal, with high R² values of 0.9972-0.9987. X-ray photoelectron spectroscopy elucidated the involvement of Al