A method for monitoring quality of loose tube fiber optic cable during manufacture in a secondary coating line , the method comprising: maintaining, in a machine-learning database , a trained machine-learning algorithm for calculating expected values of one or more quality metrics of the loose tube fiber optic cable manufactured in the secondary coating line based on values of one or more production process parameters of the secondary coating line; monitoring, by a computing system , one or more values of the one or more production process parameters of the secondary coating line during running of the secondary coating line; calculating, by the computing system , in real-time during the monitoring, one or more expected values of the one or more quality metrics using the trained machine-learning algorithm, wherein monitored values of the one or more production process parameters are used as an input of the trained machine-learning algorithm; and outputting, by the computing system, at least the one or more expected values of the one or more quality metrics to a user device ; or controlling the quality of loose tube fiber optic cable during the manufacture by calculating, in real-time during the monitoring, one or more optimal values of one or more control parameters of the secondary coating line for improving quality of the loose tube fiber optic cable being manufactured as defined by the one or more expected values of the one or more quality metrics, and causing adjusting, in real-time during the monitoring, the one or more con-trol parameters of the secondary coating line to match the one or more optimal values of the one or more control parameters. maintaining, in a machine-learning database , a trained machine-learning algorithm for calculating expected values of one or more quality metrics of the loose tube fiber optic cable manufactured in the secondary coating line based on values of one or more production process parameters of the secondary coating line; monitoring, by a computing system , one or more values of the one or more production process parameters of the secondary coating line during running of the secondary coating line; calculating, by the computing system , in real-time during the monitoring, one or more expected values of the one or more quality metrics using the trained machine-learning algorithm, wherein monitored values of the one or more production process parameters are used as an input of the trained machine-learning algorithm; and outputting, by the computing system, at least the one or more expected values of the one or more quality metrics to a user device ; or controlling the quality of loose tube fiber optic cable during the manufacture by calculating, in real-time during the monitoring, one or more optimal values of one or more control parameters of the secondary coating line for improving quality of the loose tube fiber optic cable being manufactured as defined by the one or more expected values of the one or more quality metrics, and causing adjusting, in real-time during the monitoring, the one or more con-trol parameters of the secondary coating line to match the one or more optimal values of the one or more control parameters. <EOS>