An all-too-common experience on Australian beaches is a bluebottle sting (Physalia physalis). Although rarely lethal, they can be very painful, and large swarms can place significant strain on surf lifesaving personnel to administer first aid and maintain effective beach patrols. As floating organisms with negligible swimming ability, their distribution is primarily driven by winds and ocean surface currents. To quantify drivers of bluebottle beaching and stings, we combined multiple spatially expansive and long-running (10–20 years) datasets that included observed beachings and stings in an occupancy modelling framework. This allowed us to separate the probability of beaching or stings from the probability of detecting these events (e.g., fewer swimmers means a lower probability of ‘detecting’ a sting). Overall, onshore winds (i.e., from the east; ~25–170°) during the preceding day, lead to the highest probabilities of bluebottle beachings (0.65–0.85), while offshore winds (i.e., from the west; ~225–359°) result in very low probabilities (~0.10) of beaching. To predict bluebottle beaching and stings, we employed a suite of statistical and machine learning approaches, with varying degrees of success. Answering such questions allows both the public and Surf Lifesaving Australia to make informed decisions regarding beach safety and envenomation risk.