This paper explores the integration of advanced learning algorithms, specifically Deep Neural Networks (DNNs) and ensemble methods, for enhancing resource allocation in Device-to-Device (D2D) communications. Traditional resource allocation strategies in D2D networks often struggle with dynamic and complex wireless network conditions. Our approach leverages the robust predictive capabilities of DNNs combined with the strength of ensemble techniques to address these challenges. By simulating various network scenarios, our algorithmic approach demonstrates significant improvements in allocation efficiency, predictive accuracy, and adaptability to changing network conditions compared to conventional methods. The use of federated learning frameworks further ensures privacy preservation and reduces communication overhead. This study not only offers a comprehensive analysis of advanced learning algorithms in D2D communications but also paves the way for their broader application in wireless networking