In the sphere of managing multiple, conflicting objectives concurrently, non-dominated sorting emerges as a pivotal method guiding decision-making towards optimal solutions by generating one or more Fronts. While numerous algorithms exist for multi-objective non-dominated sorting on static data points, there remains a scarcity of the same on streamed or online data points. This study focuses on the critical realm of handling real-time or online data streams to craft an algorithm specifically tailored to manage such real-time and critical data scenarios. Furthermore, this research not only introduces a novel algorithm that utilizes a simple yet effective nested list structure mechanism to perform the task of non-dominance sorting for streamed data but also evaluates its performance by checking its correctness with the existing Fast Non-dominated Sorting algorithm which is used in both Non-dominated Sorting Genetic Algorithm (NSGA-III) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The efficacy of the algorithm is also proven by showing its applicability on numerous benchmark datasets. The proposed mechanism shows complexity \(O(MN)\) in terms of space, whereas \(O(MN)\) is the time complexity in the best-case scenario, and the worst-case as well as average-case complexity for the same is \(O(MN^2)\) . Here, M denotes the number of objective functions and N indicates the population size.