Many applications require effective classification of imbalanced data, which is found everywhere. Existing classification algorithms often misclassify the minority class in imbalanced data due to the dominant class’s influence. Boosting algorithms combine basic learners to improve their performance. AdaBoost, a popular ensemble learning system, can classify general datasets well. But this algorithm will be limited misclassified samples only. The minority-classified samples are not fit for this algorithm and as it alone not readies for imbalanced data classification. This paper introduced multi-level strategy to solve imbalanced data, where combined SMOTE with AdaBoost to process unbalanced data. AdaBoost and SMOTE optimize synthetic samples, implicitly modifying update weights and adjusting for skewed distributions. The typical AdaBoost technique uses too many system resources to prevent redundant or useless weak classifiers. To make process simple applied Adaptive PSO (APSO) to the SMOTE_AdaBoost results re-initialize of strategy to the optimize AdaBoost weak classifier coefficients. Four real imbalanced datasets on six classifiers—Naïve Bayes (NB), Random Forest (RF), Multi-layer Perception (MLP), Decision Tree (DT), and K-Nearest Neighbor (KNN)—verify the proposed multi-level strategy. The proposed strategy (APSO_SMOTE_AdaBoost) is applied to six classifiers’ and compared to SMOTE-PSO. The multi-level proposed strategy outperforms with standard approach in accuracy, precision, recall, sensitivity, and F-score.