The present paper introduces a transformative framework for diffusion-weighted NMR spectroscopy using quaternion principal component analysis (QPCA), addressing fundamental limitations in conventional spectral analysis. It is evident that conventional PCA methodologies are predominantly influenced by nuisance variance sources, including baseline undulations, random concentration scaling, and thermal noise. These factors have the capacity to obscure subtle diffusion signatures entirely. While the vectorization of multiple gradient planes has been shown to amplify target signals, it has also been demonstrated that this process simultaneously disrupts intrinsic cross-gradient correlations, resulting in persistent cluster overlap. We present a novel hypercomplex solution where the reference spectrum ( \(g_0\) ) and three diffusion-weighted spectra ( \(g_1, g_2, g_3\) ) are encoded as a single quaternion entity \(\textbf{s}(\omega ) = s_{g_0}(\omega ) + s_{g_1}(\omega )\textbf{i} + s_{g_2}(\omega )\textbf{j} + s_{g_3}(\omega )\textbf{k}\) . It is asserted that this encoding preserves inter-channel correlations unique to diffusion-weighted NMR, thus distinguishing it from prior applications in signal and image processing. The ability to capture joint chemical-shift and diffusion contrasts without requiring vectorization or preprocessing is a key advantage. This representation preserves algebraic coupling between spectral planes, thereby enabling two-dimensional quaternion principal component analysis (2D-QPCA)—which applies matrix-based PCA in quaternion form to encode four spectral planes (one real reference and three imaginary diffusion-weighted) and project onto principal components, typically the first two for dimensionality reduction—to perform a coordinated rotation of the feature space that aligns diffusion contrast with the first two principal components while segregating artefacts to higher dimensions and maintaining phase relationships destroyed by vectorization. A thorough validation of synthetic and experimental nuclear magnetic resonance (NMR) data has been conducted, which has resulted in a substantial enhancement in cluster separation when compared to conventional PCA. The modified Davies-Bouldin index, a metric used to evaluate the quality of clustering in PCA, demonstrates a significant improvement, with a value of 0.586 as opposed to 0.057. 2D–QPCA achieves a separation score of 0.586, improving over PCA (0.057; x10.3) and over vectorized QPCA (0.345; x1.70). Values are the transformed separation scores (“sep”) derived from the Davies-Bouldin index. Additionally, the approach successfully suppresses macromolecular artefacts without the necessity of preprocessing, thereby ensuring the integrity and reliability of the experimental results. The regression-based implementation is known to converge after 10–20 iterations, while handling rank-deficiency through Tikhonov regularization. Beyond the scope of spectroscopy, the present work establishes mathematical foundations for quaternion machine learning and Clifford-algebraic extensions in metabolomics, thereby opening new pathways for geometric AI in high-dimensional biophysical data analysis.