This study investigates users' continuance intention towards AI painting applications by extending the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Flow Theory. A comprehensive research model is proposed, and 443 questionnaires were distributed to users with AI painting experience. The hypotheses were tested using structural equation modeling. Key findings include: 1. Confirmation significantly and positively predicts satisfaction and social impact. 2. Personal innovativeness has a significant effect on confirmation. 3. Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence showing the most significant impact. 4. Habit plays a negative moderating role in the association between social influence and continued intention to use. These findings offer valuable insights for understanding the appropriate utilization of AI painting and provide actionable directions for developers and service providers. The study contributes to the literature by adapting the ECM framework for AI painting and expanding its applicability. Implications for enhancing user experience, improving AI painting tools, and addressing user behavior are discussed. Future research could explore age and gender differences, additional moderating factors, and multiple AI painting applications.This study investigates users' continuance intention towards AI painting applications by extending the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Flow Theory. A comprehensive research model is proposed, and 443 questionnaires were distributed to users with AI painting experience. The hypotheses were tested using structural equation modeling. Key findings include: 1. Confirmation significantly and positively predicts satisfaction and social impact. 2. Personal innovativeness has a significant effect on confirmation. 3. Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence showing the most significant impact. 4. Habit plays a negative moderating role in the association between social influence and continued intention to use. These findings offer valuable insights for understanding the appropriate utilization of AI painting and provide actionable directions for developers and service providers. The study contributes to the literature by adapting the ECM framework for AI painting and expanding its applicability. Implications for enhancing user experience, improving AI painting tools, and addressing user behavior are discussed. Future research could explore age and gender differences, additional moderating factors, and multiple AI painting applications.