Data imbalance poses a significant challenge in IoT data analysis, hindering the effectiveness of machine learning models. This paper presents a comprehensive comparative study of various data balancing techniques to address this issue. We rigorously evaluate these techniques on both highly and moderately imbalanced IoT datasets, considering factors such as imbalance ratio and dataset characteristics. We identify the most effective strategies for improving classification performance through extensive experimentation. More importantly, we provide actionable insights that can be directly applied in the field. Our findings offer valuable guidance for researchers and practitioners working with imbalanced IoT data, enabling them to enhance the accuracy and reliability of their models.