Viscosity, a key property of liquids, is crucial for quality control in industries like chemical, pharmaceutical, and food processing. Traditional viscosity measurement tools, such as capillary viscometers, are costly and unsuitable for real-time monitoring. This study investigates the impact of convolutional neural network (CNN) architectures on predicting the viscosity of water – PVP solutions. Using droplet images from 14 solutions with varying water-to-PVP ratios, we evaluated three CNN architectures—SimpleModel, DeepModel, and ComplexModel. The findings highlight the influence of model complexity on prediction accuracy.