Establishing linkages between Meteorological and climatic data, and farming decision-making is a challenging task. The following paper addresses the challenges associated with this. A large amount of weather and climate information is presently available for farmers. A portion of the information is operational or already under development, and in particular, forecasting through climatic data and formation may not be suitable for farmers when it comes to the decision-making process. The best way to gain an advantage from natural factors is to consider them during decision-making and understand them in the best way possible. Meteorological information pertaining to agriculture, and climatic data, in particular, is an important aspect of planning in the context of agricultural production. Therefore, climatic conditions must be an integral part of the decision-making process. These factors can be determined by recording hourly, daily, and weekly temperature data, rainfall, solar radiation, wind speed, evaporation, relative humidity, and evapotranspiration. Artificial Neural Networks possess the capability of not just analysing the data but also learning from the data. This paper presents a predictive analysis to analyse the best crop which can be produced for specific weather conditions and also suggests a hybrid recommender system that adopts CBR - Case-Based Reasoning for enhancing the success ratio of the system. This proposed novel hybrid system is a combination of the collaborative filtering technique and case-based reasoning.The novelty of the model lies in the of district-wise agriculture data analysis for predicting future climatic conditions and recommending crops based on that climatic conditions and also considering the agriculture pattern of the district using a hybrid recommender system. © 2019 Procedia Computer Science. All rights reserved.