: Crop yield prediction is an important topic in the field of agriculture. The use of machine-learning algorithms for crop yield prediction
has gained popularity in recent years due to its potential to increase agricultural sustainability and food production. It provides an overview of
the latest developments in crop yield prediction using machine learning and provides an understanding of future implementation. The examines
different types of machine learning algorithms, like linear Regression, Decision Tree, Random Forest, KNN, Naive Bayes, and Gradient
Boosting, and their applications in crop yield prediction. Also discusses the factors that affect crop yield, such as Nitrogen(N), Potassium(K),
and Phosphorous(P) the content of the soil, along with the Humidity, Rainfall, Temperature, and pH factor of the soil. It recommends the type
of crop to be yielded concerning these factors and seasons, depending on which they can be incorporated into the machine learning models.
Thus, this helps the farmers to know the crop yield in advance to plan and choose a crop that would give a better yield. It concludes that crop
yield prediction using machine learning has enormous potential in improving agricultural sustainability and increasing food production and
that future implementation will lead to more efficient and effective agricultural practices. Later we will include the use of precision agriculture,
big data, and remote sensing and we build the website including a chat bot in the farmer's native language.
Keywords: Machine Learning,Data Analytics,Agronomics.