A processor implemented method, the method comprising: receiving, via one or more hardware processors, a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style; extracting, via the one or more hardware processors, a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits; generating, via the one or more hardware processors, a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network with an attention mechanism; generating, via the one or more hardware processors, an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN; simultaneously generating, via the one or more hardware processors, a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network; computing, via the one or more hardware processors, a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique; and predicting, via the one or more hardware processors, a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected. receiving, via one or more hardware processors, a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style; extracting, via the one or more hardware processors, a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits; generating, via the one or more hardware processors, a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network with an attention mechanism; generating, via the one or more hardware processors, an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN; simultaneously generating, via the one or more hardware processors, a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network; computing, via the one or more hardware processors, a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique; and predicting, via the one or more hardware processors, a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.