Smallholder cotton farmers face persistent challenges in adopting smart farming technologies due to high infrastructure costs, technical complexity, and inadequate localized insights. To bridge this gap, this work focuses on developing a multimodal, low-cost, and scalable data integration framework that integrates phenological observations, satellite-derived indices (NDVI, EVI), soil imagery and properties, and weather data. By employing spatial–temporal analytics and machine learning approaches, the system achieves an 83% accuracy in soil classification and an R2 of 0.85 in predicting critical soil nutrient levels. Through feature engineering and dimensionality reduction, the pipeline effectively balances depth of analysis with computational feasibility. Notably, an empirical 40–50% cost reduction compared to traditional sensor-based frameworks makes this model financially accessible for resource-constrained farmers. The results demonstrate that integrating multiple data sources and straightforward analytical methods can significantly enhance decision-making, boost productivity, and promote sustainability in smallholder cotton cultivation. This solution underscores the feasibility of affordable, localized intelligence in settings where capital and technical capacities are limited, ultimately contributing to better resource utilization and improved farming outcomes.