Fuzzy logic, particularly through Gaussian membership functions, provides a flexible approach to modeling uncertainty by representing varying degrees of truth rather than relying on binary classifications. Unlike probability, fuzzy logic quantifies the degree of membership within a set, making it well-suited for complex, real-world relationships. This chapter introduces a fuzzy linear regression model that overcomes the limitations of traditional regression techniques. The model is employed in R platform to analyze the employment-poverty relationship in selected countries of the Middle East and North Africa (MENA) region. By leveraging Gaussian membership functions, the model captures subtle variations, non-linearities, and enhances predictive accuracy. The estimated model offers valuable guidance for policymakers, providing a deeper understanding of the complex dynamics between employment and poverty.