Coagulation/flocculation process is an essential step in drinking water treatment. The process of formation, growth, breakage and rearrangement of the formed aggregates is key to enhance flocculation understanding. Artificial neural networks (ANN) are a powerful technique, and it can be used to model complex problems in several areas, such as water treatment. This work evaluated the evolution of the fractal dimension of aggregates obtained through ANN modeling in the coagulation/flocculation process conducted in high apparent color water (100 ± 5 PtCo), using alum as coagulating in dosages varying from 1 to 10 Al+3 L−1, and shear rates from 20 to 60 s−1 for flocculation times from 1 to 60 minutes. Based on raw data, ANN model resulted at optimized condition of 9.5 mg Al3+ L−1 and pH 6.1, for color removal of 90.5%. For fractal dimension evolution, ANN was able to represent from 95% to 99% of the results.