This paper addresses the critical challenge of imbalanced learning, where one class significantly outnumbers the others, leading to biased classification models. We present a comprehensive meta-analysis of existing sampling techniques, including over-sampling, under-sampling, and hybrid approaches, evaluating their effectiveness across diverse datasets and performance metrics. Furthermore, we introduce a novel algorithmic framework for adaptive sampling. This framework dynamically adjusts the sampling strategy based on real-time performance feedback, optimizing the balance between data representativeness and class equilibrium. The framework incorporates a reinforcement learning agent that learns to select the most appropriate sampling method for a given dataset and classification task, considering factors such as class distribution, feature characteristics, and model complexity. Experimental results demonstrate that our adaptive sampling framework consistently outperforms static sampling methods, achieving significant improvements in classification accuracy, precision, recall, and F1-score, particularly for the minority class. The proposed approach offers a practical and effective solution for addressing imbalanced learning problems in various domains, including fraud detection, medical diagnosis, and anomaly detection.