This research explores the development of an energy-efficient event monitoring model using Mobile Wireless Sensor Networks (MWSNs), focussing on optimal group head (GH) determination and sensor deployment. Event monitoring in WSNs requires timely and reliable detection of environmental changes, necessitating robust sensor coordination, energy efficiency, and seamless communication. Existing methodologies, including Mixed Integer Linear Programming (MILP), greedy algorithms, and reinforcement learning, have addressed various aspects of event monitoring but face limitations such as high computational cost, limited energy efficiency, and lack of flexibility in dynamic environments. To address these challenges, the proposed work introduces an Improved Sea Lion Optimization (ISLO) algorithm for GH selection, prioritising metrics like energy consumption, distance, and delay. This algorithm facilitates efficient data collection and transmission to a mobile sink while ensuring network connectivity and maximising target coverage. Additionally, the proposed solution includes a novel approach for mobile sensor deployment (MSD), which dynamically optimises sensor positions to maintain connectivity and coverage while minimising deployment costs. The methodology is validated through simulations in MATLAB, with performance metrics such as data loss rate, cost efficiency, and security compared against state-of-the-art models. The proposed ISLO-based approach is expected to enhance the accuracy, energy efficiency, and longevity of event-driven MWSNs, demonstrating significant improvements over existing solutions.