To advance the study of multicomponent Fe-Cr alloys, machine learning (ML) algorithms are utilized to predict the alloys' thermodynamic and elastic properties. The ML models were trained on a dataset of 186 alloys from a previous study, and they can predict the mixing enthalpy H, Young's modulus E, and the ratio of shear to bulk moduli G/B. An iterative active learning approach is then used to optimize the chemical composition of Fe-Cr-based alloys, simultaneously improving their thermodynamic stability and elastic properties. The target property can be predicted by using supervised machine learning algorithm ANN.This is very similar to the human brain it has three layers: The inner, the outer, and the Hidden layer which contains 20,20,50 Neurons respectively. These layers are linked with each other first Input data is given to the first input layer this layer passes the data to the hidden layer. Each neuron of this hidden layer calculates the mass and adds some bias or constant to it. It then passes the information to final output layer which is activated by a function known as rectified linear unit function and it neurons predict the final result .