Machine learning has become an integral part of numerous technological advancements, yet challenges remain in adapting models to new domains without significant data degradation. This study explores cross-domain feature adaptation techniques to enhance the generalization capabilities of machine learning models. Utilizing a blend of supervised and unsupervised learning paradigms, we implemented a neural network architecture capable of extracting invariant features from disparate data domains. The objective was to assess the impact of cross-domain adaptation on model performance across varying datasets. Our method involves the integration of domain adaptation layers within the network, which are fine-tuned using a novel loss function designed to preserve feature consistency. Experimental results demonstrate a marked improvement in model accuracy and robustness, particularly in scenarios with limited labeled data from the target domain. The findings indicate that incorporating cross-domain feature adaptation not only mitigates overfitting but also facilitates improved knowledge transfer between domains. This research opens avenues for further exploration into domain-invariant learning and its applications across diverse fields such as autonomous systems and natural language processing.