Mobile Ad-hoc Network has emerged as a key technology for next-generation networks. Though its rapid growth inspires numerous applications, it is difficult to assure Quality of Service because of its immense scaling caused due to node’s mobility, fading radio signals, and unreliable nature of the wireless channel. To efficiently utilize network resources and accomplish guaranteed Quality of Service, a novel Software-Defined Multilayered Admission Control model that embeds an intelligent Neurofuzzy Inference-based Admission Control service engine is proposed in this paper. Each node makes use of the Neurofuzzy Inference-based Admission Control service to learn, manage, prioritize, and admit data traffic according to user requirement. The service engine exploits fuzzy inference-based admission control process to assess node’s current status using Quality of Service parameters, namely, bandwidth, queue load, and Received Signal Strength Indicator to evaluate the prediction index. The prediction index not only helps in determining the strongly connected neighbors during reliable path selection process but also solely decides whether the admission control session can be admitted or rejected. Moreover, the Neuro-Multilayered Learning process of the service engine helps to self-organize and make the complete network intelligent for instantaneous decision making. The proposed mechanism not only improves the session admission between nodes but also reduces the packet drops assuring successful session completion. Performance analysis using the simulation model proves that the proposed system shows promising gains with assured throughput and low end-to-end delay and has the potential to be applied in real-world scenarios.