As a fundamental elastic property, Poisson's ratio reflects the ratio of lateral strain to the axial strain under an applied stress. It governs the distribution of stress and strain field as well as the deformation process of a material. Poisson's ratio also affects the speed of wave in material. For example, if the value is large the longitudinal wave propagates much faster than shear wave and vice versa. In seismology, It is employed to analyze rock properties and forecast the propagation of seismic waves in the Earth's crust. In the realm of the theory of elasticity, It is acceptable to have materials exhibiting a negative Poisson's ratio (NPR). Counter-intuitively, these exotic NPR materials contract under applied tension. They are termed as 'Auxetic' materials. The auxeticity of these materials lead to many special properties like enhancement of plane strain fracture resistance, increment of fracture toughness, shear modulus and even boosting of acoustic response. Several 2D materials, including graphene oxide, transition metal selenides, and transition metal halides with the stoichiometry MX (where M ￼represents elements such as V, Cr, Mn, Fe, and X represents elements like Se, Cl, Br, I), exhibit auxetic behavior. Although most auxetic materials with homogenous (NPR for all directions) values are either Porous foams or specifically designed meta-materials, John Dagdelen et. al came up with three distinctive types of auxetic crystals and discovered some new auxetic compounds. Later, Chivani et al reported a few more auxetic materials. In recent years, the substantial data generated through computational and experimental methods, along with the application of advanced machine-learning (ML) techniques, has propelled the field of materials science into a new era often described as the fourth paradigm of scientific exploration. Different Machine learning algorithms are utilized to generate efficient and accurate predictions of elastic properties of materials like Young's modulus, and Shear modulus. Typically, supervised algorithms are used to build accurate models where the model is trained using a fully labeled dataset. The prediction is then done by minimizing the error metric in both sets of problems of Classifications and Regressions. Bulk and shear modulus have been predicted with great accuracy in search of Superhard materials in these reports. Binghui et al 18 explained the difficulties of building a machine learning model to predict Poisson's ratio with high accuracy but still managed to achieve R 2 around 0.7 by random forest for a class of materials called Oxide Glass. Romain et. al described how important a supervised ML model can be to speed up the discovery of auxetic Zeolite materials. Poisson's ratio is predicted for specific binary alloys also using supervised ML. So far, the literature is missing an ML model that can predict Poisson's ratio with good accuracy for any general material.