The paper presents the hydrate inhibition system operational issues and adoptable practical solutions to overcome the challenges and ensure uninterrupted offshore gas production involving subsea long pipelines. The paper also highlights how big data and mathematical modelling can be deployed for effectively predicting hydrate inhibition requirements, gas production limitations, and performance of Mono Ethylene Glycol (MEG) regeneration units. Offshore gas production and hydrate inhibit operational upsets are mapped, extensive field data was verified and flow assurance simulations were performed. Extensive sampling of hydrate inhibitor is conducted and laboratory results are compared against modelling predictions. Combination of machine learning algorithm and physics simulators are used to predict the expected hydrate inhibitor concentration from a regeneration unit, its distribution throughout the long subsea pipelines, and its impact on hydrate inhibition and gas production. Based on the lessons learned in operating hydrate inhibition systems for offshore gas fields, effective strategies for field facilities design and optimization are developed. Real-time visualization of lean MEG concentration distribution throughout the MEG loop and its impact on gas production during the winter season is developed to facilitate effective decision making on gas production based on actual MEG concentration estimated through soft sensor analytics solution. The paper highlights the applicability of a combination of machine learning algorithms and physics simulators for effectively solving complex problems related to the hydrate inhibition in long subsea pipeline.