Till date, in material science literature, mostly various kinds of supervised models have been developed to predict different properties. These approaches fail to predict exotic properties due to the lack of data.In this work, we have demonstrated a new methodology where a semi-supervised anomaly detection algorithm is used to predict the Auxetic materials despite a severe lack of data on auxetic materials. Moreover, a regression model is built to predict the Poisson's ratio for non-auxetic material. This model also helps in determining the optimal features for learning the Poisson's ratio, which in turn helps in building the descriptors for Anomaly detection. Therefore we present a general framework to predict the poisson's ratio as shown in Figure . This complete framework exhibits versatility, as it can be applied to the prediction of various material properties beyond Poisson's ratio. Particularly, it is well-suited for properties that exhibit both rare and normal regimes, such as negative and positive refractive indices. Fig. A schematic of how both the models can be utilized to get the Poisson's ratio of an unknown material.