The forward premium puzzle is one of the most notable puzzles in the foreign exchange market. Seminal studies in the field began in the first half of the 1980s. Since then, several methodologies have been proposed and tested, aiming to explain the occurrence of forward premium puzzle. However, despite the researchers efforts, no unequivocal solution has been fund. The objective of this dissertation is to analyze this anomaly in the macroeconomic context of emerging countries considering the approach of the non-rational expectations. To that end, adaptive learning techniques were applied, following the methodologies proposed by Chakraborty and Evans (2008) and Reed (2019), where the modeling of agents expectations about the future spot exchange rate is conducted using learning algorithms and remain close to the rational expectations solution, but with deviations generated from past forecast errors. The results corroborate those found in the current body of literature, suggesting that adaptive learning can provide an explanation for the forward premium puzzle also when considering the exchange rate of emerging market currencies against the US dollar, both for the single-state model and for the two-state model. The simulations results reproduce the main empirical features found in the analyzed sample and express the importance of the monetary fundamentals persistence to explain the negative bias in the forward premium coefficient.