Agriculture forms the backbone of many developing countries, including India. Accurate crop yield estimation can give societies a better handle on food security and resource management. Existing studies on crop yield and harvest prediction apply deep learning as well as machine learning models. Various crop parameters like soil type, climate, water content, and so on have been used to predict crop yield. More advanced techniques include the use of satellite imagery and optical and SAR data along with plant indices like NDVI. Machine learning algorithms, including K-NN, SVM, and random forest regression, have been widely used for yield estimation. Deep learning approaches work by extracting salient and relevant features from images or non-visual data to estimate crop production. Networks like 3D-CNNs, LSTMs, and auto-encoders have achieved significant improvement in accuracy in estimating crop yields from satellite images. This paper aims to summarize the techniques and models being used for the purpose of yield estimation along with limitations, and possible areas of further study.