In this work, we carried out electrochemical studies on ASTM A615 (bare steel rebar), ASTM A767 (steel rebar with hot dip galvanized zinc coating), and ASTM A1094 (steel rebar with continuously galvanized zinc coating) rebars exposed to two different environments. In one condition, the samples were exposed to a simulated concrete pore solution (SCPS) containing 3.5 wt.% NaCl. Over a period of 12 months, the electrochemical properties of the samples were regularly assessed through open circuit potential (OCP), linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) on a weekly basis. In the other condition, steel rebars were embedded in concrete with water-to-cement ratio of 0.53. A controlled surface area of the cast concrete block was exposed to a 3.5 wt% NaCl solution using a dam mounted on it. This method allowed for the introduction of chlorides into the reinforced concrete while maintaining control over the exposure process. Under these conditions, the rebars were continuously monitored by carrying out OCP and EIS tests for a period of up to three years since curing. Based on the experimental results obtained, we developed a mathematical framework that combines mechanistic and machine-learning concepts for analyzing the behavior of the rebars in both conditions. EIS analysis was utilized to quantify the transports processes, activation, and interface interaction of the rebars with the corrosive environments in each condition. EIS served as tool to quantify the transports processes, activation mechanisms, and interface interaction of the rebars within corrosive environments across diverse conditions. We conducted this analysis using a Time Series Prediction (TPS) approach of several phase angle plots along 300 days of rebars in pore solution and 900 days of rebars in reinforced concrete, which leveraged recurrent neural networks techniques to predict corrosion mechanisms. This approach allowed us to learn dynamically from real-time measurements, eliminating the sole reliance on domain expertise for parameter optimization. Finally, we utilized our comprehensive experimental-theoretical framework, which integrated Electrochemical Impedance Spectroscopy testing, to make long-term predictions for the performance of the rebars using neural networks techniques. These predictions spanned several years and were based on rigorous analysis. To validate the accuracy and reliability of our framework, we compared the predictions with the experimental results, thereby confirming the accuracy and reliability of our predictions.