<p>The application of Artificial Intelligence (AI) in agriculture has revolutionized soil fertility observation and estimation of yield, hence enhancing productivity and efficiency. The present study gives a new Central Moment Skewness with Component Regression Fully Connected Neural Network (CMS-CRFCNN) architecture to deal with some of the fundamental problems in examining soil fertility and forecasting yields. The goal of this study is to create an integrated system where Central Moment Skewness-based pre-processing is combined with Exp-Sine-Squared kernel-based Principal Component Regression and Softmax Fully Connected Neural Network in order to suggest a novel method for the prediction of crop yield that will solve the problems of skewness in the data, non-linear interaction between features, and computational cost—factors usually given little consideration in previous studies. The suggested model runs in two stages: pre-processing and prediction. Pre-processing entails the usage of Central Moment Skewness Normalization to normalize feature distributions and log-likelihood adjustment for very skewed features. Prediction entails the use of Exp-Sine-Squared kernel-based Principal Component Regression in extracting soil fertility features, whereas Softmax Fully Connected Neural Network is used in producing final crop yield predictions (CYP). With the Crop Recommender Dataset with Soil Nutrients, the model was evaluated against the current state-of-the-art solutions on five measures—precision, recall, accuracy, training time, and Mean Absolute Error (MAE). The findings indicate that CMS-CRFCNN performs superior to current state-of-the-art solutions with improved accuracy, improved training time, and reduced error rates. With the incorporation of state-of-the-art pre-processing and predictive modeling, this work delivers a scalable, effective, and AI-based solution for sustainable agriculture and CYP decision-making that is future-proof.</p>