Telecommunication networks have a complex structure, and failures occurring in individual network elements can significantly affect the reliability and stability of the entire system. This paper analyzes the application of Machine Learning (ML) models for remote diagnosis of network elements in telecommunication networks. Several ML-based approaches, such as Naive Bayes, Support Vector Machine (SVM), Random Forest, and Artificial Neural Networks, are studied and compared in terms of their effectiveness in detecting network anomalies and failures based on key performance parameters such as packet loss, latency, traffic load, and resource utilization. Mathematical and probabilistic models are used to describe the diagnostic process and estimate the probability of failure in dynamic network conditions. Simulation and experimental results show that ML-based diagnostic models achieve up to 30% higher diagnostic accuracy than traditional statistical monitoring methods. The research results confirm that machine learning techniques significantly increase the efficiency, reliability, and automation of remote network diagnostics and provide a foundation for the development of intelligent, flexible, and real-time network monitoring systems.