Unsupervised domain adaptation (UDA) has emerged as a critical area of research to address the challenge of domain shift, where the statistical distribution between the labeled source domain and the unlabeled target domain differs significantly. Traditional supervised learning methods fail to generalize effectively in such scenarios, as they rely heavily on labeled data from the target domain, which is often unavailable. This paper introduces a novel hybrid framework that integrates adversarial training, Maximum Mean Discrepancy (MMD), and self-training techniques to improve the robustness and accuracy of UDA. The proposed methodology leverages adversarial training to align feature distributions between the source and target domains by employing a domain discriminator and a feature extractor in a min-max optimization framework. MMD further minimizes the distance between source and target feature distributions in a reproducing kernel Hilbert space (RKHS), ensuring statistical alignment. Additionally, self-training iteratively refines pseudo-labels for target domain samples, enabling the model to improve its predictive accuracy on unlabeled data. The framework is implemented using Python and validated on synthetic datasets designed to simulate real-world domain shift scenarios. Comprehensive experimental results demonstrate the effectiveness of the proposed approach in achieving domain invariance and enhancing classification accuracy. Visualizations of feature alignment and performance metrics highlight the significant improvements brought about by the integration of these techniques. This research contributes to the growing body of UDA literature by providing a robust, scalable, and interpretable framework. The findings have practical implications for various applications, including computer vision, natural language processing, and speech recognition, where labeled target data is scarce or unavailable. Future work will explore the extension of this framework to complex real-world datasets and the integration of advanced techniques such as contrastive learning and transformer-based architectures for further performance enhancement.