For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles, surface craft, and small aircraft is rapidly increasing. Applications include scientific studies, solar-wind-wave energy harvesting, transport and distribution of goods, naval operations, security, acoustic surveillance, communication, search and rescue, marine pollution, ocean cleanup, conservation, fisheries, aquaculture, mining, and monitoring and forecasting. Designing optimal paths leads to cost savings, longer operational time, and environmental protection. Our goal is to develop and apply our optimal planning theory and methodology to increase the efficiency of surface craft and underwater vehicles operating in uncertain dynamic ocean conditions. For the first time, we combine environmental forecasting with stochastic control and risk theory, and employ fundamental partial-differential-equations (PDEs) and efficient level-set solutions for exact reachability and path planning. Our novel proposed ocean applications include energy-optimal path planning, optimal environment harvesting, optimal cleanup, and information-optimal exploration and Bayesian machine learning.