Artificial intelligence (AI) and machine learning (ML) have become essential tools for automating decision-making across a wide range of applications. However, a significant challenge in classification tasks is the presence of imbalanced datasets, where the minority class is underrepresented compared to the majority class. This imbalance often leads to biased models that perform well on the majority class but fail to accurately predict the minority class, which can be crucial in domains such as fraud detection, medical diagnosis, and predictive maintenance. This chapter explores the development of specialized algorithms designed to handle imbalanced datasets in classification tasks. We review existing methods, including data-level approaches like oversampling and under sampling, as well as algorithmic-level strategies such as cost-sensitive learning and ensemble methods. Furthermore, we propose a novel hybrid approach that combines multiple techniques to enhance model performance on imbalanced datasets. The effectiveness of the proposed method is demonstrated through extensive experiments on various benchmark datasets, showing significant improvements in the classification of minority class instances. This work contributes to advancing the field of AI/ML by providing robust solutions for one of the most persistent challenges in classification tasks. This book chapter explores various algorithms developed to tackle this issue, emphasizing their effectiveness, applicability, and comparative performance. The study investigates diverse techniques, including sampling methods, algorithmic adjustments, and ensemble learning strategies customized for managing imbalanced data. Furthermore, the book chapter evaluates these algorithms based on critical model performance metrics such as, recall, F1-score, precision, and accuracy, offering insights into their practical implications and limitations.