The automobile industry is one of the world’s largest manufacturing sectors and a key contributor to economic growth. Demand forecasting plays a critical role in supply chain management within the automotive sector. Reliable forecasts are essential for production planning, inventory control, and meeting market demands efficiently. However, accurately predicting demand remains a challenge due to the influence of external factors such as socioeconomic trends and weather conditions. This study presents a systematic literature review of the forecasting methods employed within the automotive industry, encompassing both vehicle and spare parts demand. Following PRISMA guidelines, 63 publications were identified and analyzed, covering traditional statistical models such as ARIMA and SARIMA, as well as state-of-the-art artificial intelligence approaches, including artificial neural networks. The review finds that classical statistical models remain prevalent for vehicle demand forecasting, Croston’s method dominates spare parts forecasting, and AI-based techniques increasingly outperform conventional models in recent studies. Furthermore, the review compiles a broad set of external variables influencing demand and highlights the common challenges associated with demand forecasting. It concludes by outlining potential directions for future research.