A system configured for an inverse inference of a chain of a manufacturing process to predict a configuration of the manufacturing process for desired properties of a product, the system comprising: at least one memory storing a plurality of instructions; one or more hardware processors communicatively coupled with the at least one memory, wherein the one or more hardware processors are configured to execute one or more modules; a receiving module configured to receive a description of a plurality of unit manufacturing processes of the manufacturing process and a set of desired output properties from the manufacturing process, wherein the plurality of unit manufacturing processes are in a predefined chain; a description module configured to create an ontological description of the plurality of unit manufacturing processes and its one or more parameters; a learning module configured to learn a plurality of Bayesian network models for each of the plurality of unit manufacturing processes, wherein the learned plurality of Bayesian network models are stored in a knowledge repository; a selection module configured to select two or more unit manufacturing processes of the plurality of unit manufacturing processes; an obtaining module configured to obtain the learned Bayesian network model corresponding to each of the selected two or more unit manufacturing processes from the knowledge repository; a validation module configured to validate compatibility among the obtained each of the Bayesian network model corresponding to each of the selected two or more unit manufacturing processes using a set of predefined rules, wherein the Bayesian network model corresponding to each of the selected two or more unit manufacturing processes are compatible if the output of first Bayesian network model is ontologically compatible to the input of the second Bayesian network model, wherein the ontological type of the output variable of the first ontological network model is either the same as that of the input variable to the second ontological network model or the output of first Bayesian network model is a specialization of the input to the second Bayesian network model; and a generation module configured to generate a composite model using compatible Bayesian network model corresponding to each of the two or more selected unit manufacturing processes to predict the configuration of the plurality of unit manufacturing processes, wherein the composite model is sampled to infer configuration for desired properties of the product to be manufactured, and wherein the generated composite model is a Bayesian network model for the predefined chain obtained by appending successive Bayesian network model of each unit manufacturing process of the plurality of unit manufacturing processes. at least one memory storing a plurality of instructions; one or more hardware processors communicatively coupled with the at least one memory, wherein the one or more hardware processors are configured to execute one or more modules; a receiving module configured to receive a description of a plurality of unit manufacturing processes of the manufacturing process and a set of desired output properties from the manufacturing process, wherein the plurality of unit manufacturing processes are in a predefined chain; a description module configured to create an ontological description of the plurality of unit manufacturing processes and its one or more parameters; a learning module configured to learn a plurality of Bayesian network models for each of the plurality of unit manufacturing processes, wherein the learned plurality of Bayesian network models are stored in a knowledge repository; a selection module configured to select two or more unit manufacturing processes of the plurality of unit manufacturing processes; an obtaining module configured to obtain the learned Bayesian network model corresponding to each of the selected two or more unit manufacturing processes from the knowledge repository; a validation module configured to validate compatibility among the obtained each of the Bayesian network model corresponding to each of the selected two or more unit manufacturing processes using a set of predefined rules, wherein the Bayesian network model corresponding to each of the selected two or more unit manufacturing processes are compatible if the output of first Bayesian network model is ontologically compatible to the input of the second Bayesian network model, wherein the ontological type of the output variable of the first ontological network model is either the same as that of the input variable to the second ontological network model or the output of first Bayesian network model is a specialization of the input to the second Bayesian network model; and a generation module configured to generate a composite model using compatible Bayesian network model corresponding to each of the two or more selected unit manufacturing processes to predict the configuration of the plurality of unit manufacturing processes, wherein the composite model is sampled to infer configuration for desired properties of the product to be manufactured, and wherein the generated composite model is a Bayesian network model for the predefined chain obtained by appending successive Bayesian network model of each unit manufacturing process of the plurality of unit manufacturing processes.