The Impact of Machine Learning Based Framework on Network Traffic Performance in Mobile Ad Hoc Networks

System learning has become a well-liked instrument for promoting efficient and environmentally friendly decision-making in a growing number of settings. Mobile Ad Hoc Networks (MANETs) are particularly challenging settings because of their dynamic topologies, erratic communication, and frequently unpredictable traffic patterns. This research presents the usefulness of a machine learning-based general framework for improving the performance of community visitors in MANETs. This technique uses support vector machines (SVM) for different visitor kinds, assesses the type's correctness for unique scenarios, and makes use of community optimization tools for improved overall performance. An extensive performance assessment using a range of network scenarios and visitor types shows how well the suggested architecture handles community website visitors' performance. The results show that the suggested framework can correctly identify the styles of the visitors and modify the community parameters to enhance performance. Furthermore, the suggested method can be applied without issue in real-world circumstances and can improve the overall performance of the conventional system.