Intent-Based Networking (IBN) focuses on technologically independent, fast, and reliable interaction between network infrastructure management systems and users. This concept is being developed to automate and accelerate the deployment of the network lifecycle. Thus, IBN includes mechanisms for recognizing, understanding, and extending the intents that define the quality-of-service (QoS) requests. Therefore, this paper proposes an intent-based software-defined wireless network (IBSDWN) approach in which the controller intelligently decides when to initiate the handover of services. For this purpose, the controller selects the access point (AP) to which the client device should connect, based on the set quality of experience (QoE) requirements using an intent processing technique. A method for initiating handover in IBSDWN based on machine learning (ML) algorithms and an integral QoE criterion formed from real-time measurements of parameters: received signal strength indication (RSSI), throughput, packet loss, and delay is developed. Implementation of ML module in IBN architecture for monitoring system allowed to reduce the volume of signal traffic in communication channels between network equipment and controller. Also, the developed ML module made it possible to detect the degradation of QoE values and prevent situations when the user is not satisfied with the received QoS for adaptive prediction of the moment of network reconfiguration. On the basis of a simulation model, it is proved that the proposed solutions can improve the quality of experience of multimedia services to end-users.