A significant concern during oil and gas production is flow assurance to avoid loss of time and money. Due to production conditions changes (such as pressure and temperature), especially in the Brazilian pre-salt region, the solubility of the components of the crude oil (oil-gas-water) can decrease, resulting in the formation of deposits. The fouling is usually caused by wax, gas hydrate, and inorganic salt (scale). In this work, models were developed using Machine Learning strategies for scale formation monitoring and measuring process parameters associated with remediation of obstruction from other sources. First, models for the calcium carbonate scaling formation process in the presence of monoethylene glycol (typical gas hydrate inhibitor) were created using feedforward neural network architecture to predict the differential pressure (deltaP) one and five steps ahead, obtaining an R2 higher than 92.9 percent for the training and test group for both the prediction horizon. The second approach explored was the development of models for determining the pH in atmospheric and pressurized systems (up to 6.0 MPa) using image analysis. These models could be applied to control and monitor the Nitrogen Generation System, which can be used for different flow assurance problems, and its kinetics strongly depend on the system s pH value. This step initially created classification models for the system pH (2, 3, 4, 5, 6, 7, 8, 9, 10) using the Convolution Neural Networks (CNN), Support Vector Machine, and decision tree architectures. Also, CNN models were built to predict the pH in the range of 2-10.