Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring, Internet of Things (IoT) and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes for monitoring and surveillance of a targeted region. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more challenging. In such case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to manage each cluster as well as the overall network efficiently. WSN empower applications for critical decision-making through collaborative computing, communication and distributed sensing. However, they face several challenges due to their peculiar use in a wide variety of applications. One of the inherent challenges with any battery operated sensor is the efficient consumption of energy and its effect on network lifetime. The topology management becomes very important as nodes are often distributed randomly that leads to uneven distribution of load. In addition, cluster head selection plays an important role in enhancing network lifetime and improving energy efficiency. Inappropriate selection of Cluster Head (CH) may lead to high network overheads resulting in early battery depletion affecting overall network lifetime. The sensor node selected as CH is responsible for both inter and intra cluster communication, therefore, it consumes more energy as compared to other cluster nodes. Thus, it is very important to select an optimal node as CH and to efficiently rotate the CH role periodically to avoid network partitioning problem. In this research work, a novel Grid based Hybrid Network Deployment (GHND) approach for WSN is proposed to ensure energy efficiency and load balancing. The new merge and split technique that evenly distributes the nodes across the network for maximizing energy efficiency and network lifetime. The proposed method is compared with existing state-of-the-art energy efficient cluster and grid-based techniques on the basis of energy efficiency, scalability and network lifetime. An extensive set of simulations and experiments reveal that the proposed method outperforms existing state of the art techniques such as LEACH and PEGASIS in terms of load balancing, network lifetime, and energy consumption. Moreover, the cluster head selection problem is resolved with a multi-criteria decision modeling using the Analytical Network Process (ANP). A mathematical framework is developed that takes into account various parameters such as residual energy level, distance from neighboring nodes, centroid distance, number of times a nodes has been cluster head and whether a node is merged or not, for efficient selection of cluster head. In the ANP model, these mentioned parameters are pairwise compared to obtain the weights through the supermatrix. The supermatrix is transformed in to a limit matrix that reports priority weights for all criteria parameters. These priority weights are further used to optimize the criteria list for efficient cluster head selection by eliminating low weight parameters ultimately minimizing computational complexity of the ANP process. The sensitivity analysis of the proposed ANP based scheme has been carried out to check the stability of parameters and relative importance in CH selection process.