<p>Unsupervised Domain Adaptation (UDA) focuses on bridging the gap between source and target domain distributions. Existing UDA approaches often struggle to capture the diverse contextual dependencies required to address ambiguities in visual feature representations. To overcome these challenges, we propose a framework called Consensus Augmented Masking for Subspace Alignment (CAMSA) that leverages multiview representations to enhance contextual diversity and establish a consensus subspace for improved domain alignment. Firstly, multiple models are independently trained with distinct masking augmentations to ensure prediction consistency and extract specialized multiview features, each capturing a unique contextual perspective. These multiview features are unified into a low-rank structure via sparse subspace representation, enabling cross-view consensus and robust domain alignment. The unified representation is further optimized by constructing a consensus affinity matrix, which facilitates the learning of a projection matrix to embed multiview features into a latent subspace. Within this latent space, source domain prototypes and <i>k</i>-means clustering on the target domain are used to estimate conditional probabilities for downstream tasks. Extensive empirical evaluations on standard benchmark datasets highlight the exceptional performance of CAMSA, consistently surpassing state-of-the-art UDA methods across a variety of architectures and configurations, underscoring the importance of leveraging diverse contextual views for robust domain alignment.</p>