Traditional crop yield prediction methods often rely on statistical models that are based on historical data and do not account for the complex interactions among environmental factors, soil, and crop genetics. Given the abundance of data accessible in agriculture, Machine learning algorithms can analyse large and heterogeneous datasets and identify patterns and relationships that traditional methods cannot capture. The main objectives of the review include: to explore machine learning and deep learning techniques used to predict the crop yield using various input parameters, to evaluate the accuracy measures considered for measuring the performance of these techniques, to examine the efficiency of these techniques, to explore the input parameters used for modelling, to compare the performance of algorithms for crop yield prediction and to explore the efficiency of hybridized models. In the current study, we performed a systematic literature review (SLR) to get insight of algorithms, input parameters and evaluation parameters used in the relevant papers. The target crops for the study are corn/maize and soyabean. We were able to identify 1859 related papers from four databases, out of which we selected 82 papers for further analysis after considering inclusion and exclusion criteria. The results of SLR shows that the most popular machine learning algorithm among researchers of selected domain are Random forest, Artificial Neural Networks (ANN), Support Vector Machines and XG Boost. Additionally, LSTM and CNN are the most widely used deep learning techniques for crop prediction. From the literature reviewed, it has also been shown that temperature, precipitation, crop historical yield, NDVI and ph-value are the most considered input parameters by the researchers of the selected domain. The current review has also shown the inclination towards the hybridization of models for enhancing the model accuracy.