Several solutions were attempted in order to efficiently group packets based on their availability.Our study examined various different applications of decision trees. Using these trees, messages were classified into hundreds of categories.The seed set and branching depth of the decision tree were optimized for proximity to the ideal solution. The preceding explanation increases the need for higher storage capabilities while also increasing the time needed to execute a search.There are ways that employ decision trees to identify the best pair and multi-pair for each packet. The importance of multi-match packet classification has grown in tandem with the popularity of more sophisticated network applications. It is critical to evaluate not only the primary factor itself to ensure that all of the results that match the main factor can be retrieved. Effective classification strategies must be discovered to avoid the difficulties of this procedure.To improve the efficiency of box sorting, a unique approach known as "boundary cutting" has been devised.Typical packet categorization applications necessitate significant matching. The demonstrated method has two key advantages.By substituting rule boundaries for predefined gaps, the suggested method improves on earlier approaches to border-cutting.The aforementioned parameters have a significant impact on the quantity of RAM required.Removing boundaries makes internal nodes' data processing more challenging, yet binary search excels in this area.