With millions of users relying on telecom services for connectivity, even minor network disruptions can lead to significant financial and reputational damage. To address these challenges, machine learning (ML) is emerging as a transformative tool for predictive maintenance, allowing telecom operators to detect and address potential failures before they escalate. This is especially crucial in Asia, a region experiencing unprecedented digital transformation and rapid 5G expansion.
The Growing Need for Predictive Maintenance in Telecom Networks
Telecom networks are vast and complex, comprising various hardware, software, and connectivity layers. The expansion of 5G, coupled with increasing data demands, has further complicated network management.
The Asia-Pacific telecom market, valued at USD 18.12 billion in 2025, is projected to reach USD 23.97 billion by 2030, driven by rapid 5G adoption, with over 60% of global 5G subscribers expected in the region by 2027. To support the annual 30-40% surge in mobile data traffic, telecom operators in India, China, and Southeast Asia are aggressively expanding fiber-optic and 5G networks, ensuring seamless connectivity and meeting the region’s growing digital demands.
Telecom companies have previously relied on reactive maintenance (fixing problems after they occur) or preventive maintenance (conducting routine checkups based on time-based schedules). However, these approaches are often inefficient, leading to unexpected downtimes and high operational costs.
Predictive maintenance powered by ML offers a data-driven alternative, as AI-driven predictive analytics reduce 5G equipment failures by up to 35%, ensuring 99.99% uptime. By analyzing vast amounts of network data—including sensor logs, performance metrics, and historical failure patterns—ML algorithms can identify potential failures before they occur. This enables telecom operators to proactively address issues, reducing unplanned downtimes, optimizing maintenance schedules, and ultimately lowering costs.
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How Machine Learning Enhances Predictive Maintenance
Machine learning models utilize real-time and historical data to predict potential network failures. These models analyze various factors, such as:
- Signal degradation and abnormal usage patterns
- Temperature and power fluctuations in network equipment
- Environmental conditions affecting network performance
- Traffic congestion and unexpected network loads
By leveraging ML, telecom companies can shift from a reactive to a proactive approach, ensuring higher service reliability and improved customer satisfaction.
Telecom companies across Asia are leveraging machine learning (ML) and AI-driven predictive maintenance to enhance network reliability, reduce downtime, and optimize operational efficiency.
Lintasarta, in partnership with 6Estates, is investing in cutting-edge GPU infrastructure to enhance ML capabilities, ensuring more efficient data processing for applications that require significant computational power. Similarly, SK Telecom and Nokia are pioneering AI-driven fiber sensing technology, which gathers data from commercial networks using ML.
In Japan, NTT DATA is integrating AI/ML-powered smart factory inspections to streamline industrial processes, demonstrating how predictive ML applications extend beyond telecom into broader infrastructure maintenance. Hidehiko Tanaka, Head of Technology and Innovation at NTT DATA, emphasized the importance of converging robotics and AI/ML with predictive maintenance.
Robotics and AI will greatly contribute to reducing human workload in all industries.
Similarly, AIS is driving ML-led autonomous networks for 5G. At the 2024 Mobile Broadband Forum, Wasit Wattanasap, Head of Nationwide Operations and Support Business Unit at AIS, highlighted the company’s focus on AI, ML, and advanced network orchestration to create a more intelligent and scalable telecom infrastructure that enhances operational efficiency.
Advancements Driving Adoption in Asia
The Asia Pacific is the fastest-growing region in the global ML market, projected to expand at a compound annual growth rate (CAGR) of 37.6% between 2022 and 2030, according to Fortune Business Insights. The region’s rapid digital transformation—driven by e-commerce, digital banking, and 5G deployment—is accelerating the need for advanced network maintenance solutions.
Key factors contributing to the rise of ML-driven predictive maintenance in Asia include:
- Government Support for ML: Countries like South Korea, China, and Singapore are heavily investing in AI and ML research. For example, South Korean President, Yoon Suk Yeol, has announced plans to allocate KRW 9.4 trillion (USD 6.94 billion) towards artificial intelligence (AI) investments by 2027.
- Edge Computing Integration: By processing data closer to the source, edge computing enhances the effectiveness of predictive maintenance. In dense 5G environments, where low-latency and high-reliability maintenance are crucial, ML-driven edge solutions play a pivotal role.
- Cross-Industry Adoption: While telecom is a key driver, other industries—including finance, healthcare, and manufacturing—are also leveraging ML for predictive maintenance. The broader acceptance of ML technologies across sectors is fueling further innovation in telecom.
Also Read: Edge Computing Fuels Innovation Across Asia’s Key Industries
Future Outlook: Towards Self-Healing Networks
Looking ahead, ML-driven predictive maintenance is expected to evolve into fully autonomous network management. Future developments include:
- Self-Healing Networks: ML will not only predict failures but also autonomously correct them without human intervention.
- Blockchain and IoT Integration: Secure, transparent maintenance records stored on blockchain, combined with real-time monitoring via Internet of Things (IoT) sensors, will enhance predictive capabilities.
- Quantum Computing for Faster Processing: Through quantum computing advancements, ML models will process network data even faster using methods like principal component analysis (PCA), embeddings, and statistical correlations to simplify features, leading to highly precise predictive maintenance solutions.
By shifting from reactive to proactive maintenance strategies, AI-driven solutions are reducing downtime, optimizing operations, and enhancing network reliability. As 5G adoption accelerates and AI technology advances, predictive maintenance will become an essential tool for telecom operators, paving the way for smarter, more resilient networks across the region.
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