Chemometric techniques have emerged as powerful tools in analysing the chemical and physical properties of wood, aiding in species authentication, quality assessment, and sustainability efforts. This chapter examines the current chemometric methods used to analyse Dalbergia latifolia wood highlighting techniques that are valuable for its identification and analysis. This includes spectroscopy-based techniques such as near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, coupled with multivariate data analysis methods. Principal component analysis (PCA), partial least squares regression (PLSR), linear discriminant analysis (LDA), and machine learning algorithms are becoming increasingly important for improving predictive modelling and classification accuracy aiding in distinguishing D. latifolia from other similar species. Chemometric applications also extend to determining provenances, studying degradation, and detecting adulteration in timber products. Emerging trends indicate a growing integration of artificial intelligence, portable spectroscopic devices, and hyperspectral imaging to facilitate rapid, non-destructive analysis. The chapter highlights the advantages, limitations, and future prospects of chemometric techniques in D. latifolia research, emphasizing their role in supporting conservation strategies, sustainable forestry, and regulatory measures to combat illegal logging.