• Shubneet
• Navom Saxena
• Anushka Raj Yadav
• Navjot Singh Talwandi

Artificial intelligence and machine learning (AI/ML) are reshaping IT infras tructure by enabling intelligent solutions across networking and IoT domains. This paper investigates the application of deep learning for optimizing high traffic network routing [1] and explores machine learning-driven cost optimization strategies in IoT ecosystems [2]. Through comparative analysis, we highlight how these AI/ML approaches deliver significant improvements in performance and operational efficiency. Our findings are contextualized within the broader land scape of AI research, drawing on recent advances in deep learning architectures and their real-world deployments. The study demonstrates that integrating state of-the-art neural networks and predictive analytics can yield up to 30% gains in resource utilization and cost savings. This work offers actionable insights for practitioners aiming to leverage AI/ML for scalable, efficient, and adaptive IT systems.

Artificial Intelligence, Machine Learning, Network Optimization, IoT Cost Optimization, Deep Learning

"Integrating Artificial Intelligence and Machine Learning Across IT Domains: Advances in Networking, Security, Finance, IoT, and Energy Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j350-j355, May-2025, Available :http://www.jetir.org/papers/JETIR2505A35.pdf

"Integrating Artificial Intelligence and Machine Learning Across IT Domains: Advances in Networking, Security, Finance, IoT, and Energy Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj350-j355, May-2025, Available at : http://www.jetir.org/papers/JETIR2505A35.pdf