WSN is the collection of nodes which are capable of sensing, computing and communicating the data in the network. WSN is used in various applications such as military surveillance, application based on environmental monitoring, agriculture, structural health and smart parking. But the major challenge of this important technological trend is the limited power available for its operation, which is one of the research gaps attracting intellectual mind globally to resolve The ant colony algorithm is a type of optimization algorithm that simulates the foraging behavior of ants. Its basic principle originates from the shortest path problem encountered by ants in nature. In this research, the sensor nodes were clustered effectively using K-means technique and improved parallel Ant colony optimization (ACO) algorithm for finding the shortest path to transmit the data. The experimental result using K-means technique and parallel Ant colony optimization (ACO) algorithm for finding the shortest path to route the data performed 98% better compared with Particles Swarm Optimization (PSO). The study established that the higher the k-means cluster partition employed during clustering, the better the results obtained from ACO. The choice of ACO was necessitate due to its faster convergence compared to other existing optimization method.