Frequent Subgraph Mining (FSM) is the process of identifying common subgraph patterns that surpass a predefined frequency threshold. While FSM is widely applicable in fields like bioinformatics, chemical analysis, and social network anomaly detection, its execution remains time-consuming and complex. This complexity stems from the need to recognize high-frequency subgraphs and ascertain if they exceed the set threshold. Current approaches to identifying these patterns often rely on edge or vertex extension methods. However, these strategies can introduce redundancies and cause increased latency. To address these challenges, this paper introduces a novel approach for identifying potential k-vertex patterns by combining two frequently observed (k - 1)-vertex patterns. This method optimizes the breadth-]first search, which allows for quicker search termination based on vertices count and support value. Another challenge in FSM is the validation of the presumed pattern against a specific threshold. Existing metrics, such as Maximum Independent Set (MIS) and Minimum Node Image (MNI), either demand significant computational time or risk overestimating pattern counts. Our innovative approach aligns with the MIS and identifies independent subgraphs. Through the "Maximal Independent Set" metric, this paper offers an efficient solution that minimizes latency and provides users with control over pattern overlap. Through extensive experimentation, our proposed method achieves an average of 10.58x speedup when compared to GraMi and an average 3x speedup when compared to T-FSM