The ability to predict how air transport demand evolves is one of the key elements for effective air mobility planning. At the core of this lies the indispensable correlation between demand and supply. Understanding the evolving dynamics of demand is a fundamental requirement to intervene in infrastructure, resize services, optimize available resources, and plan infrastructure investments. In recent years, air transport has recorded remarkable growth rates, making new management and development strategies for infrastructure necessary. In this context, airport planning plays a central role for airport operators, who, through the Masterplan, define the development plan of an airport, addressing weaknesses where possible and enhancing strengths. Based on these considerations, this study develops an econometric model for forecasting air transport demand, applied to a concrete case: Cagliari – Elmas International Airport [1, 2]. The first part of the study focuses on the description of the methodology. After a literature review on air transport demand models, various methodological approaches were evaluated, including regression models, moving average models, and Box-Jenkins, Auto Regressive Integrated Moving Average (ARIMA). The comparative analysis led to the selection of a multivariable model, combining independent variable forecasts obtained with ARIMA models and the use of multiple linear regression. This approach was replicated for Cagliari – Elmas Airport, resulting in a 15-year air transport demand forecast under three different scenario hypotheses. Finally, the results were compared with evolutionary trends proposed by international observers and regulatory authorities, highlighting a strong convergence of findings.