<p>Wireless sensor networks (WSN) are widely used in multiple applications, where data security is more crucial due to communication in dangerous and unattended areas. Hence, with the ever-increasing number of connected devices, WSNs are now presented with a whole range of newly emerging issues from managing millions of sensor nodes to ensuring secure delivery of data. While adopting supervision applications like surveillance, forest fire detection, military uses, etc. the WSNs are more prone to security threats, attacks and tampering than traditional networks. Therefore, there is a need to design an efficient security-aware scheme in WSNs to minimize constraints such as energy, communication, bandwidth and memory for enhancing data security. To solve the challenges of secure data transmission and energy efficiency with limited energy resources in WSN, a new architecture named Crossover Boosted Sea Lion Optimized Ensemble Voting Machine Learning (CBSLO-EVML) is proposed. In the perception layer, sensor nodes collect data and transfer it to the cluster head which in turn identifies the optimum route to the sink node. The network layer deals with data transfer across gateways and sieving the data to minimize the charges to be met. The application layer is secured using encryption procedures. This model incorporates an ensemble voting mechanism with the Sea Lion optimization algorithm to enhance attack detection and overall performance. By employing the crossover-boosted trigger strategy the CBSLO-EVML model effectively ensures significant network requirements, enhancing the security from attacks and tampering. The proposed method provided efficient performances with an average delay of 0.08 seconds, energy consumption of 490 µJ, and security value of about 99%. The proposed model outperforms existing approaches in terms of significant evaluation measures. The overall analysis is performed to attain a better performance of the proposed approach for attaining better security for WSNs in various applications.</p>