A processor implemented method, comprising: obtaining, via one or more hardware processors, an input training data comprising historical details of one or more entities, historical loan information corresponding to the one or more entities, and one or more corresponding recommended covenants; performing, via the one or more hardware processors, a data exploration analysis on the input training data to obtain one or more covenants of at least one of a first covenant category and a second covenant category; training, by using a binary technique via the one or more hardware processors, a first machine learning model based on the input training data and one or more covenants of the first covenant category to obtain a first trained machine learning model; obtaining, via the one or more hardware processors, at least a subset of the input training data; and iteratively performing, via the one or more hardware processors, for each covenant of the second covenant category, until a number of predicted covenants corresponding to the second covenant category is less than or equal to an iteration count, to obtain a second trained machine learning model: processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, the second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration. obtaining, via one or more hardware processors, an input training data comprising historical details of one or more entities, historical loan information corresponding to the one or more entities, and one or more corresponding recommended covenants; performing, via the one or more hardware processors, a data exploration analysis on the input training data to obtain one or more covenants of at least one of a first covenant category and a second covenant category; training, by using a binary technique via the one or more hardware processors, a first machine learning model based on the input training data and one or more covenants of the first covenant category to obtain a first trained machine learning model; obtaining, via the one or more hardware processors, at least a subset of the input training data; and iteratively performing, via the one or more hardware processors, for each covenant of the second covenant category, until a number of predicted covenants corresponding to the second covenant category is less than or equal to an iteration count, to obtain a second trained machine learning model: processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, the second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration. processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, the second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration.