Drowning is a critical global health issue, responsible for over 236,000 deaths annually. Lifeguards play a key role in preventing drowning incidents by continually monitoring bathers and detecting hazards taking place in highly dynamic environments such as pools. Previous studies have observed that specialized drowning detection training is closely associated with enhanced detection of drowning events. However, the neural mechanisms underlying this greater drowning detection performance remain unclear. Here, we address this gap in the literature by comparing brain function between lifeguards and novices, and examining changes in brain dynamics associated with drowning detection training. Using a dynamic functional connectivity analysis method called Leading Eigenvector Dynamics Analysis (LEiDA), we analysed time-varying patterns of brain activity in 18 lifeguards and 16 novices during a drowning detection task and at rest. Our findings revealed significant differences within group and between groups in the probability of occurrence of attention-related brain networks, particularly the frontoparietal, ventral attention and Default Mode networks. These findings provide novel insights into the neural basis of lifeguard expertise and how specialized training shapes neural mechanisms and improves drowning detection performance in critical lifesaving scenarios.