The growing variety of hardware, protocols, and vendor-specific configurations poses significant challenges to maintaining network infrastructure in multi-vendor environments. Conventional manual methods of network management are no longer adequate because of their restricted scalability, slow deployment times, and high mistake rates. The intelligent automation methodology presented in this research aims to standardize and accelerate network administration in diverse vendor ecosystems. Critical operations like VLAN provisioning, firmware upgrades, configuration backup, real-time monitoring, and self-healing are automated by the framework through the integration of open-source tools like Ansible, Netmiko, Nornir, Prometheus, and Grafana, as well as AI-driven fault detection. The system was evaluated using a realistic multi-vendor prototype that included devices from Juniper, Cisco, and MikroTik. According to experimental data, our proposed network minimizes human error through intelligent automation, greatly increases recovery rates (94–95%) from network outages, and cuts execution time by more than 90%. Scalability is supported by the system's tiered architecture, and fault tolerance is improved by including predictive maintenance. Additionally, by moving away from manual configuration and towards NetDevOps methods, which promote automation, scripting and proactive monitoring, the framework transforms the role of network experts.

Keywords: Network Automation, Multi-Vendor Infrastructure, Ansible, Self-Healing Network, NetDevOps, AI Monitoring, Intelligent Network Infrastructure, Prometheus Monitoring, Scalable Network, Open-Source Tools, Automatic Fault Detection.

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Source of Funding:

The authors declare that no specific grant from any funding agency in the public, commercial, or not-for-profit sector was received for this study.

Competing Interests Statement:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.

Consent for publication:

The authors declare that they consented to the publication of this study.

Availability of data and materials:

The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.

Institutional Review Board Statement:

Not applicable for this study.

Informed Consent:

Not applicable for this study.

Acknowledgements:

The authors appreciate the welcoming atmosphere at work. Authors also want to thank all of their co-workers and supporters.