With the increasing intensity of traffic in wireless networks, driven by the rapid proliferation of Internet of Things (IoT) devices, video streaming applications, and mobile services, the demand for intelligent and efficient approaches to bandwidth management is becoming more urgent. This study proposes a methodology for traffic modeling and control based on a Deep Neural Network (DNN), emphasizing the role of intuitionistic fuzzy evaluation (IFE) in enhancing the accuracy and adaptability of decision-making under uncertainty. The developed model integrates a Recurrent Neural Network of the Long Short-Term Memory (LSTM) type to effectively capture temporal dependencies in network traffic data. Intuitionistic fuzzy sets are applied to formalize the degrees of membership, non-membership, and hesitation when evaluating network states and predictions, thereby improving the model's robustness to noise and data instability. The proposed IFE-based evaluation enables the assessment of the accuracy of LSTM outputs. The results highlight the significance of the combined use of deep learning, intuitionistic fuzzy evaluation, and federated approaches for achieving intelligent, reliable, and resilient resource management in next-generation wireless networks.