Recently, enormous culture profiles and datasets from biomanufacturing processes to produce recombinant therapeutic proteins (RTP) such as monoclonal antibodies (mAbs) could be generated by virtue of the advancement in process analytical techniques and artificial intelligence (AI). Thus, now it is highly necessary to develop AI-based data-driven models (DDMs) and exploit them accordingly in order to further enhance operational efficiency and accelerate reliable product supply. Since bioprocess is a complex and dynamic system, DDMs are practical and particularly useful to describe the intrinsic relationship among biological and process parameters and cell culture conditions by capturing inherent patterns and to produce high-quality RTP under consistent operations as well as to decrease cost and time by predicting incipient or abrupt faults during the cell cultures. In this work, we provide the practical guideline for choosing the best DDM on given mAb-producing Chinese hamster ovary (CHO) cell culture data sets, enabling us to forecast culture performance such as VCD, and mAb titer as well as glucose, lactate and ammonia concentrations in real time manner. Via the case study with 32 fed-batch data sets of CHO cell cultures, we suggested best combination of model elements including AI algorithms and multi-step ahead forecasting strategies, for good prediction in terms of the computational load as well as the model accuracy and reliability, which is applicable to implementation of interactive data-driven model within bioprocess digital twins. We believe this systematic study can help bioprocess engineers to start developing predictive DDMs with their own data and learn how their cell cultures behave in near future, thereby making proactive decision possible.