Wireless Sensor Networks (WSNs) are an inevitable technology prevalently used in various critical and remote monitoring applications. The security of WSNs is compromised by various attacks in wireless mediums. Even though, various attacks are present, the black hole attack degrades the network performance and resource utilization, resulting in poor network lifetime. Therefore, the proposed research suggests an effective Intrusion Detection System for WSN to detect and classify black hole attacks based on ensemble ML classifiers. The BDD dataset is used for the analysis which is subjected to Chicken Swarm Optimization based feature selection. The selected features are balanced through SMOTE and TOMEK based STL data balancing module. An ensemble of five baseline ML classifiers such as SMO, NB, J48, KNN and RF utilizing voting ensemble approach is suggested to classify the attacks in the dataset. The performance of the algorithm is analyzed through evaluation metrics such as accuracy, precision, recall and F1-score. The comparison of proposed model with six ML and DL classifiers exposes the superiority of the proposed model’s classification performance.