Pain experienced during medical procedures is a significant healthcare and psychological concern, particularly among pediatric patients. While Virtual Reality (VR) shows promise in pain reduction, its effectiveness is often limited by a lack of real-time adaptability and physical interaction. This study addresses this gap by integrating a Deep Q-Network (DQN)-based adaptive model and tactile feedback into a personalized VR exposure therapy for managing pain during pediatric venipuncture procedures. This randomized controlled trial enrolled 50 participants, consisting of 46 children aged 4–16 years, two 3-year-olds, and two participants aged 17 and 19 years. Participants were assigned to either a static VR group (Group A, n = 25) or an adaptive VR group with DQN and a sensor-equipped stress ball (Group B, n = 25). Pain was assessed post-procedure using the Wong-Baker FACES Pain Rating Scale (0–10), with secondary measures including physiological data (heart rate) and user experience ratings. Results showed that children in Group B reported significantly lower pain levels (mean = 1.8 ± 1.4) compared to Group A (mean = 5.0 ± 2.6, p < 0.001), with a large effect size (Cohen’s d ≈ 1.53) and multiple zero-pain cases (6/25 in Group B vs. 0/25 in Group A). Additionally, 84% of participants in Group B rated the session as “Very Effective,” compared to 36% in Group A. The DQN-enhanced VR system, equipped with sensor-driven tactile feedback, not only improved immersion and reduced pain perception but also dynamically adapted in real time to physiological stress indicators. These findings highlight the potential of adaptive VR as a non-pharmacological intervention for pediatric pain management.