Mining the pattern of interesting items plays a significant role in data analysis and decision-making strategies of real-time applications. Often the term 'interest' in pattern discovery denotes the frequency of the pattern. In recent research, the domain of data mining is considering the utility of the item instead of frequency, which indicates often profit. This manuscript argues that neither utility nor frequency of the itemset alone influence the target objective. Moreover, the profit is not only the utility factor of the itemset, apart from profit, the objectives like storage, saleability and other domain specific requirements can also be the utility factors. In regard to this argument, the manuscript endeavoured to define a novel model that discovers the top-k high utility patterns under multiple utility objectives (HUPM-MUO). The experimental study was carried on various datasets, which portray the performance advantage of the proposed model over the other contemporary models.