For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles (AUVs), surface craft, and small aircraft is rapidly increasing. The challenge of creating time-optimal navigation routes for these vehicles has many applications, including ocean data collection, transportation and distribution of goods, naval operations, search and rescue, detecting marine pollution, ocean cleanup, conservation, and solar-wind-wave energy harvesting, among others. In this thesis, we employ the Massachusetts Institute of Technology – Multidisciplinary Simulation, Estimation, and Assimilation Systems (MIT-MSEAS) time-optimal path planning theory and schemes based on exact Hamilton–Jacobi partial differential equation (PDE) and Level Set methods to predict and study the sensitivity of reachable sets and time-optimal trajectories in the Portugal–Azores–Madeira region of the Northern Atlantic, for several types of missions and autonomous ocean vehicles. Specifically, using the MIT-MSEAS multi-resolution ocean modeling and data assimilation system to provide four-dimensional ocean currents in the region, we compute time-reachable sets and time-optimal paths for several missions, and examine the sensitivity to variations in vehicle type, speed, start time, voyage direction, and operating depths. Our real-data-driven multi-resolution simulation study illustrates how navigational paths vary with these parameters, and how ocean dynamics and variability in the Portuguese ocean regions affect the time optimization, as compared to direct voyages in the absence of any ocean currents. We also highlight effects of the Azores and Madeira archipelagos, differences between surface and bottom path planning, interception routes between vehicles of different speeds, and the utilization of arrival time fields in planning. Results showcase how principled path planning, integrating data-driven multi-resolution ocean modeling with exact reachability theory and numerical schemes, can assess the capabilities of ocean vehicles in the Portugal–Azores–Madeira ocean region, by predicting the fastest travel time, expected range, and optimal headings, for varied types of ocean missions.