Estimating the time of arrival (ETA) for vehicular travel is central to modern transportation and logistics systems. This thesis presents an end to-end pipeline for processing raw GPS data, map matching trips with Valhalla, and training a deep learning model capable of accurately predicting ETAs based on historical information alone. The scope covers an extensive dataset drawn from a major metropolitan region (Paris), capturing over 2 million trips in 2023 for training/validation and an additional 400,000 trips in 2024 for final testing. To address data quality issues, robust preprocessing filters were applied to eliminate unstructured or anomalous sessions. Trip boundaries were defined by time gaps and idle periods, and each trip underwent map matching to align latitude/longitude points with OpenStreetMap road segments.