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Abstract A framework for agricultural yield forecasting and fertilizer suggestion is amongst the most critical demands of the moment. Water shortages as well as other soil degradation have grown increasingly prevalent in recent years, causing enormous loss of human life and livelihood all across the world. Crop output unpredictability has increased as a result of increasing incidences of global climate change and fluctuating weather. Due to the unpredictability, agricultural productivity has likewise been difficult to anticipate. This necessitates the development of a crop yield forecast and fertilizer suggestion method, which has been accomplished in this research article with minimal error. As a result, in order to improve the process of agricultural production prediction, this study examines the deployment of machine learning technologies. Linear clustering, Artificial Neural Networks, and Decision Making are all used in this research's technique. The technique has been assessed for the existence of any errors that have resulted in good operational outcomes.Keywords Linear Clustering, Artificial Neural Network, and Decision Making.

Copyright © 2025 Zaware Payal Eknath. This is an open access article distributed under the Creative Commons Attribution License.