Chemical and biological processes often involve the dissolution of substances in a solvent. In this work, we describe a possibility for automation to monitor such dissolution processes to increase the efficiency of the overall process. A Faster Region-based Convolutional Neural Network with a ResNet50 backbone was trained to monitor and automate the dissolution process of particles in various solutions. A dataset was created, containing four different solutions that show the dissolution process at various stages. The results demonstrate that the model is capable of detecting medium to large particles, especially those with no or minimal overlap with other particles. Additionally, the model was able to monitor colored solutions, such as potassium permanganate solution. The model demonstrates strengths in detecting medium to large particles. By expanding the dataset, the detection performance for smaller particles can be improved, leading to an overall enhancement of the model’s capabilities. Automating this process is a crucial step toward fully autonomous laboratories.