As Next Generation (NextG) networks become more complex, it has become increasingly necessary to utilize more advanced algorithms to enhance the robustness, autonomy, and reliability of existing wireless infrastructure. One such algorithm is network traffic prediction, playing a crucial role in the efficient operation of real-time and near-real-time network management. The contributions of this thesis are twofold. The first introduces a novel cluster-train-predict framework that leverages domain knowledge to identify unique timeseries sub-behaviors within aggregates of network data. This method produces distributions that are more robust towards changes in the spatio-temporal environment. The ensemble of time-series prediction models trained on these distributions posses a greater affinity towards accurate network prediction, selectively employing learned behaviors to handle sources of time-series data without any prior knowledge of it. This approach tends to improve the ability to accurately forecast network traffic volumes. Secondly, this thesis explains the development and implementation of a modular data pipeline to support the cluster-train-predict framework under a variety of conditions. This setup promotes repeatable and comparable results, facilitating rapid iteration and experimentation on current and future research. The results of this thesis surpass traditional approaches in [1] by up to 60%. Furthermore, the effectiveness of this framework is also validated using two additional time-series datasets [2] and [3], demonstrating the ability of this approach to generalize towards other time-series data and machine learning applications in uncertain environments. / Master of Science / As Next Generation (NextG) networks become more complex, it has become increasingly necessary to utilize more advanced algorithms to enhance the robustness, autonomy, and reliability of in-use wireless infrastructure where network traffic prediction plays a crucial role in the efficient operation of real-time and near real-time network management. The contributions of this thesis are twofold. The first explores a novel cluster-train-predict framework that uses an unsupervised learning approach, specifically time-series K-means clustering, to group similar time-series data. In doing so, this approach identifies unique time-series behaviors within network provider data. Since this approach aims to reduce the variance within each aggregate, models can specialize towards particular network behaviors, becoming better suited for a wider variety of network trends during prediction. Because this framework assigns data to each cluster based on these groupings, the framework can adapt towards changes in network infrastructure or underlying shifts in its environment to forecast with a greater degree of certainty and explainability. This framework can even generalize towards out-of-distribution cases where it has no prior knowledge of a source of time-series data outperforming [1] by up to 60%. This approach tends to improve the ability to accurately forecast network traffic volumes. Secondly, this thesis explains the development and implementation of a modular data pipeline to support the cluster-train-predict framework under a variety of conditions with repeatable and comparable results, facilitating rapid iteration and experimentation on current and future research. The results of the framework are also corroborated on two, additional time-series datasets [2] and [3], demonstrating the ability of this approach to generalize towards time-series data, where this framework can also be applied to other machine learning applications in uncertain environments.