The class imbalance is challenging issue in machine learning and data mining especially health care, telecom sector, agriculture sector, and many more (Zhu et al. in Pattern Recogn Lett 133:217–223, 2020; Thabtah et al. in Inf Sci 513:429–441, 2020). Imbalance of data samples across classes can arise as a result of human error, improper/unguided data sample selection, and so on (Tarekegn et al. in Pattern Recogn 118:107965, 2021). However, it is observed that applying imbalanced datasets to the data mining and machine learning approaches, it retains the biased in results which leads to the poor decision-making (Barella et al. in Inf Sci 553:83–109, 2021; Zhang et al. in ISA Trans 119:152–171, 2021; Ahmed and Green in Mach Learn Appl 9:100361, 2022). The primary motivation for this research is to explore and develop novel ensemble approaches for dealing with class imbalance and efficient way of retrieving synthetic data. In this paper, an ensemble method called IPSO-SMOTE-AdaBoost is developed to solve the class imbalance problem by combining the synthetic minority oversampling technique (SMOTE) (Gao et al. in Neurocomputing 74:3456–3466, 2011; Prusty et al. in Prog Nucl Energy 100:355–364, 2017), improved particle swarm optimization (PSO) (Yang et al. in J Electron Inf Technol 38:373–380, 2016), and AdaBoost. AdaBoost combined with SMOTE provides an optimal set of synthetic samples, thereby modifying the updating weights and adjusting for skewed distributions. The typical AdaBoost approach, on the other hand, consumes far too many system resources to avoid redundant or ineffective weak classifiers. With the proposed ensemble framework, IPSO-SMOTE-AdaBoost, parameters can be re-initialized to counter the concept of local optimum as well with the SMOTE that is boosted with AdaBoost method. The proposed method is validated using three datasets on six classifiers: extra tree (ET), naive Bayes (NB), random forest (RF), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). After that, the IPSO-SMOTE-AdaBoost is compared to the existing SMOTE-PSO. The evaluation of proposed work is done with measures, namely accuracy, precision, recall, sensitivity, and F-score, and result shows that the proposed technique outperformed the usual PSO and SMOTE variations.