###### Abstract
_Sustainability_  
0000-0002-4000-0002-4000][PERSON]; [PERSON]; [PERSON]; [PERSON], [PERSON] Predicting Prices of Staple Crops Using Machine Learning: A Systematic Review of Studies on Wheat, Corn, and Rice. _Sustainability_  
0000-0002-4000-0002-4000-0002-4000][PERSON]; [PERSON]; [PERSON]; [PERSON] Predicting Prices of Staple Crops Using Machine Learning: A Systematic Review of Studies on Wheat, Corn, and Rice. _Sustainability_  
0000-0002-4000-0002-4000-0002-40002-40002-40002-40002-40002-40002-4002-4002-40002-4002-40002-40002-4002-40002-4002-4002-4002-40002-4002-policy, international trade, and food system stability. Fluctuations in their production and price have consequences for both consumers and producers. [PERSON] and [PERSON], in their research [5], note that with grain prices near or at historical lows, the world's average caloric intake increased, leading to gains in health and life expectancy, but at the same time, concerns have been raised since the level of intensive cultivation needed leads to biodiversity loss, soil degradation, chemical pollution, aquifer depletion, and soil salinity. While striving to mitigate these risks, the goal is to increase the production of wheat, corn, and rice in the years to come to feed the projected 10 billion people by 2050. Agricultural commodities are being traded in global markets. This comes with risks and benefits for all parties involved. On one side, market mechanisms can help establish fair prices by allowing farmers and agribusinesses to \"lock\" prices through futures contracts and options, stabilize their income, and support long-term planning. On the other side, the commodity markets can be influenced by speculative activity and thus make prices highly volatile and decoupled from fundamentals. Additionally, smallholder farmers in some developing countries may lack the infrastructure, information, or financial skills to utilize and benefit from the market systems [6]. The price volatility in agricultural commodities poses significant challenges for farmers, traders, consumers, and policymakers alike. Sudden and unpredictable changes in prices can destabilize income, disrupt planning for farmers, and lead to food insecurity, social unrest, and inflation [7]. Volatility also adds additional levels of complexity in policymaking, as governments struggle to design safety nets for the uncertain conditions farmers and society face. Price swings are led by weather events, pests and diseases, geopolitical tensions, trade restrictions, and financial speculation [8]. Therefore, managing price volatility is essential to building stable and equitable food systems [7]. In response to the risks posed by price volatility, farmers, policymakers, traders, agribusinesses, researchers and practitioners have used advanced forecasting methods to improve price prediction. Agricultural markets have complex behavior that is characterized by non-stationarity, non-normality, and non-linearity in both supply (arrivals) and price data. This poses significant challenges for traditional statistical forecasting models. These difficulties are further compounded by external volatility from climate events, market speculation, and policy interventions. Such dynamics limit the effectiveness of classical approaches. Traditional econometric models such as ARIMA (Autoregressive Integrated Moving Average) and SARIMA (Seasonal Autoregressive Integrated Moving Average), have been used for time-series forecasting, but the limitations they have in handling data that is non-linear and high-dimensional, have led to a shift to other approaches [9]. Machine learning (ML), ensemble learning (EL), and deep learning (DL) methods are powerful alternatives. These techniques can capture complex patterns in large and diverse datasets. These models are increasingly being applied to forecast commodity prices using as input historical price time series, weather data, production statistics, and even textual information from news and social media to analyze the market's sentiments.