Customer segmentation remains a critical challenge for businesses operating in dynamic market environments. This paper presents a novel hybrid approach integrating reinforcement learning (RL), clustering, and classification methods with extended RFM-D analysis for dynamic customer segmentation. The proposed methodology combines 4 key behavioral metrics: Recency, Frequency, Monetary value, and Diversity of purchases to create comprehensive customer profiles. Our approach employs 3 complementary machine-learning strategies: Q-learning for adaptive discount optimization, k-means clustering for unsupervised customer grouping, and ensemble methods for supervised classification. RL component enables real-time strategy adaptation based on customer response patterns, while Diversity metric captures purchasing breadth beyond traditional RFM parameters. Experimental validation demonstrates the effectiveness of the hybrid approach. RL method achieved optimal profit maximization, while k-means clustering successfully identified four distinct customer segments with targeted discount strategies. XGBoost model showed superior performance. Results indicate that Diversity metric strongly correlates with purchase Frequency, enabling more precise customer targeting. The adaptive discount strategy dynamically adjusts based on price elasticity, resulting in personalized customer engagement that maximizes customer satisfaction and business profitability in evolving market conditions.