Summary The formation of deposits is a very common issue in oil and gas pipeline transportation systems. Such sediments, mainly wax and paraffine for crude oil, or hydrates and water for gas, progressively reduce the free cross-sectional area of the pipe, leading in some cases to the complete occlusion of the conduit. The overall result is a decrease in the transportation performance, with negative economic, environmental, and safety consequences. To prevent this issue, the amount of inner deposits must be continuously and accurately monitored, such that the corresponding cleaning procedures can be performed when necessary. Currently, the former operation is still dictated by best-practice rules pertaining to preventive or reactive approaches, yet the demand from the industry is for predictive solutions that can be deployed online for real-time monitoring applications. The paper moves toward this direction by presenting a machine learning methodology that leverages pressure measurements to perform online monitoring of the inner deposits in crude oil trunklines. The key point is that the attenuation of pressure transients within the fluid is dependent on the free cross-sectional area of the pipe. Pressure signals, collected from two or more distinct locations along a pipeline, can therefore be exploited to estimate and track in real time the presence and thickness of the deposits. Several statistical indicators, derived from the attenuation of such pressure transients between adjacent acquisition points, are fed to a data-driven regression algorithm that automatically outputs a numeric indicator representing the amount of inner pipe debris. The procedure is applied to the pressure measurements collected for one and a half years on discrete points at a relative distance of 40 and 60 km along an oil pipeline in Italy (100 km length, 16-in. inner diameter pipes). The availability of historical data prepipe and postpipe cleaning campaigns further enriches the proposed data-driven approach. Experimental results demonstrate that the proposed predictive monitoring strategy is capable of tracking the conditions of the entire conduit and of individual pipeline sections, thus determining which portion of the line is subject to the highest occlusion levels. In addition, our methodology allows for real-time acquisition and processing of data, thus enabling the opportunity for online monitoring. Prediction accuracy is assessed by evaluating the typical metrics used in the statistical analysis of regression problems.