Climate and weather forecasting have always been problematic, and predicting meteorological factors like temperature, humidity, and rainfall is crucial for agriculture, forestry, and commercial companies because climatic conditions are unpredictable. Existing methods use only machine learning, data mining, and dimensionality reduction. This proposed work uses hybrid models to improve meteorological accuracy. A backpropagation neural network (BPNN) and a support vector regression model (SVR) have been used in the proposed work to understand linear and non-linear patterns in weather conditions like average temperature, minimum temperature, maximum temperature, average relative humidity, and rainfall. Different integrations of BPNN and SVR were tested with pre and post-estimation. The proposed work applies SVR to the data to make predictions. The linear SVR models effectively capture linear patterns and compute the residual error sequence by subtracting the SVR predictions from the original series. BPNN models the residual error sequence, and the final prediction is made by merging SVR and BPNN predictions. These models were implemented singly and then compared to hybrid models. The hybrid models’ experimental results are statistically promising for the datasets, and the proposed work outperforms different state-of-the-art methods.