Optical fiber networks have become the backbone of modern telecommunications due to their high capacity, low latency, and cost-efficiency. Ensuring reliable Quality of Trans- mission (QoT) in these networks requires accurate estimation of performance metrics prior to lightpath deployment. Traditional approaches often rely on worst-case assumptions to account for uncertainties that might arise, for instance, from amplified spontaneous emission (ASE) noise introduced during optical amplification, leading to conservative de- sign margins that reduce spectral efficiency and increase operational costs. This thesis investigates the use of gray-box modeling techniques, which integrate analytical models with Machine Learning (ML), to enhance the prediction accuracy of gain and loss profiles across an Optical Line System (OLS).