The Rafflesia Optimization Algorithm is a recently developed swarm intelligence optimization approach, drawing inspiration from Rafflesia’s natural biological principles. The three stages of the algorithm are the fruiting, planting, and pollination phases. To discover the best answer, the ROA algorithm searches locally in the first stage. By cutting down on the number of individuals, it increases execution efficiency and solution correctness in the second stage. In order to exit the local optimum, it conducts a global search in the third step. A major obstacle to the overall effectiveness of Wireless Sensor Networks (WSNs) is the battery energy constraints of sensor nodes that are dispersed throughout a given region. An appropriate cluster head set can boost message transmission, prolong the lifespan of the sensor network, and sensibly regulate energy usage. This study uses the ROA algorithm to solve the optimal cluster head selection technique, employing the energy consumption of each round as an adaptation function. When compared to the LEACH, PSO, and HFPSO algorithms, ROA can enhance message transmission, extend the lifetime of the WSN, and accelerate the convergence of identifying the ideal set of cluster heads.