Gas lift is a principal technique used for artificial lifting and Enhanced Oil Recovery (EOR) method, facilitates oil flow in mature fields those in the latter half of their productive life by injecting high-pressure gas into the wellbore to reduce column density. However, the thermodynamics of gas injection in wells requiring high differential pressures introduce severe flow assurance challenges. The intense Joule-Thomson cooling effect across injection chokes is the primary driver of gas hydrate formation. In the studied field (Aghajari), the current hardware-based mitigation strategy employs Thermal Chokes, which utilize the enthalpy of live crude oil to heat the injection gas. Despite this, operational evidence indicates that during cold seasons and for wells with high pressure drops, this system proves inefficient, leading to freezing in injection lines and flow interruption. In the absence of inhibitor injection systems, operators are compelled to resort to reactive measures such as flaring injection gas to induce pressure shocks and clear blockages. This vicious cycle not only results in capital loss but also leads to production deferment and excessive workload for human resources. This research aims to propose a proactive process-based solution by synergizing data mining and computational intelligence. Through the analysis of 5,960 operational records from 101 wells (extracted from the WIMS system), an Artificial Neural Network (ANN) model was developed to serve as a virtual sensor, predicting gas thermodynamic behavior and post-choke temperature with 98.5% accuracy. The core novelty of this study lies in the simulation and validation of a dual-stage pressure reduction strategy. Results demonstrate that splitting the pressure drop profile reduces cooling intensity by up to 60%, maintaining the fluid outside the hydrate stability zone throughout the expansion path. This approach enhances safety and production stability while eliminating the need for costly physical interventions.