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Like many large, long-lived animals, whales face increased disturbance from human activities across their habitat range and life history phases. An effective approach to mitigate anthropogenic impacts on whale populations can be to reduce overlap in space and time between whales and human activities, which requires a solid understanding of whale ecology within dynamic marine ecosystems. In this talk, I will present two case studies where we have undertaken broad-scale data collection, oceanographic assessments, time-series analyses, and species distribution modeling to understand the ecological drivers of whale habitat use patterns and effectively predict their distribution to inform management efforts. Off the Oregon, USA coast whale entanglements in fishing gear pose a threat to endangered and threatened populations. I will describe our collaborative efforts to develop fine-scale spatial predictions of whale distributions in Oregon waters and assess dynamic overlap patterns with fishing gear. Results from our work have already influenced regulatory decisions on fishing effort to reduce entanglement risk to whales. In New Zealand, we recently documented a population of blue whales that share a primary foraging ground with industrial activities including oil and gas extraction, shipping traffic, and fishing effort. We analyzed both visual and acoustic observations of whale occurrence to understand the drivers of blue whale distribution patterns and the spatial-temporal lags between oceanographic factors and whale foraging. Ultimately, we develop models to forecast blue whale habitat that allows for dynamic management with up to 3 weeks lead time and are now operationalized via a user-driven application. Although the ocean is dynamic and whale movement patterns are broad, their distribution patterns are non-random, allowing for application of robust statistical ecological approaches to better understand whale habitat use and enhance management decisions in the marine environment.