A smart manufacturing network connects machines via sensing, communication, and actuation networks. The data generated from the networks are used in data-driven modeling and decision-making to improve quality, productivity, and flexibility while reducing the cost. This dissertation focuses on improving the data-driven modeling of the quality-process relationship in smart manufacturing networks. The quality-process variable relationships are important to understand for guiding the quality improvement by optimizing the process variables. However, several challenges emerge. First, the big data sets generated from the manufacturing network may be information-poor for modeling, which may lead to high data transmission and computational loads and redundant data storage. Second, the data generated from connected machines often contain inexplicit similarities due to similar product designs and manufacturing processes. Modeling such inexplicit similarities remains challenging. Third, it is unclear how to select representative data sets for modeling in a manufacturing network setting, considering inexplicit similarities. In this dissertation, a data filtering method is proposed to select a relatively small and informative data subset. Multi-task learning is combined with latent variable decomposition to model multiple connected manufacturing processes that are similar-but-non-identical. A data filtering and modeling framework is also proposed to filter the manufacturing data for manufacturing network modeling adaptively. The proposed methodologies have been validated through simulation and the applications to real manufacturing case studies. / Doctor of Philosophy / The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to model the manufacturing network to improve product quality. The data quality directly affects the modeling performance and decision effectiveness. However, the data quality is not well controlled in a manufacturing network setting. In this dissertation, we propose a data quality assurance method, referred to as data filtering. The proposed method selects a data subset from raw data collected from the manufacturing network. The proposed method reduces the complexity in modeling while supporting decision effectiveness. To model the data from multiple similar-but-non-identical manufacturing processes, we propose a latent variable decomposition-based multi-task learning model to study the relationships between the process variables and product quality variable. Lastly, to adaptively determine the appropriate data subset for modeling each process in the manufacturing network, we further proposed an integrated data filtering and modeling framework. The proposed integrated framework improved the modeling performance of data generated by babycare manufacturing and semiconductor manufacturing.