The exponential growth of telecommunication networks and digital communication, coupled with the growth in internet access, mobile devices, and emerging technologies, has amplified the significance of telecommunication networks. These telecommunication networks are the backbone of industries, including e-commerce, health care, finance, and transportation, facilitating real-time data exchange and seamless global connectivity. However, telecommunication outages have consequences on telecommunication networks which can be far-reaching, affecting businesses, governments, emergency services, and everyday users. Again, unplanned downtime disrupts critical services, leading to financial losses, compromised user experiences, and potential risks to public safety. In this study, machine learning (ML) models were utilized to predict telecommunication outages using Convolutional Neural Network (CNN), Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor, and Logistic Regression (LR) and secondary data acquired from the National Communication Authority in Ghana. Data preprocessing was done to remove empty values or variables which were then segmented into 70:10:20 and used to train, validate, and test the models, respectively. The experiment results show that CNN achieved the highest accuracy of 92.57% with a precision of 0.91, while RF achieved the lowest accuracy of 89.99% with a precision of 0.90%, with fuel issues and generator faults being the major factors of telecommunication outages.