In today’s highly competitive and customer-centric business environment, effective complaint management is essential to retaining consumer trust and satisfaction. Traditional complaint handling processes, often manual and time-consuming, have proven inadequate in addressing the speed and personalization demanded by modern customers. This study explores the strategic application of Artificial Intelligence (AI) optimization models in managing customer complaints across various sectors. The research investigates how AI technologies such as chatbots, Natural Language Processing (NLP), sentiment analysis, and machine learning algorithms are transforming complaint resolution by improving response times, accuracy, and overall customer experience. Using a mixed-method approach, data was gathered from organizations currently employing AI-driven complaint systems and customers who have interacted with such systems. Findings revealed that AI significantly enhances the efficiency of complaint handling, automates repetitive tasks, and provides data-driven insights for proactive service improvements. Moreover, AI tools were found to improve personalization in responses and detect emotional cues, which contributes to better customer engagement. Despite these advantages, the study highlights challenges such as the risk of depersonalization, algorithmic bias, and the need for continuous training of AI models. To maximize the benefits, a hybrid strategy that combines AI tools with human oversight is recommended. The study concludes that AI optimization models offer scalable and intelligent solutions to complaint handling, leading to improved customer satisfaction and operational efficiency. Future research should further explore industry-specific adaptations and the long-term impact of AI integration on customer relationship management.