Early diagnosis is essential for successful mitigation of pitting corrosion, a localized form of corrosion that causes cavities and structural failure in metallic materials. Although ultrasonic inspection techniques are effective in detecting uniform wall thinning, they have challenges in accurately identifying pitting corrosion. The present work proposes a technique for early-stage pitting detection utilizing time-lapse pulse-echo signals. The generation of two-dimensional time-lapse images of ultrasonic reflectivity may be achieved by capturing several ultrasonic traces over a period of time. These images can then serve as input for a neural network trained specifically for the purpose of pitting diagnostics. To obtain a substantial training dataset required for training the machine learning model, a drilling experiment was conducted, and random time-ordered combinations of the pulse-echo measurements produced the time-lapse images. A classification neural network was trained to detect the presence of pits, while a separate regression network was trained to estimate the pit depth. Test data from a previously unseen transducer indicates that the pit depth estimations exhibit a mean absolute error of less than 0.2 mm. All pits are reliably identified when they exceed the defined pitting threshold of 0.5 mm by a depth of 0.1 mm.