The pressing need for precise prediction of soil strength and state across various environmental and engineering applications has catalyzed the development of sophisticated predictive models. This paper introduces "SoilPredict," a comprehensive machine learning architecture designed to enhance the accuracy and reliability of soil predictions. The architecture is segmented into several specialized modules: Data Preprocessing, Feature Selection, Transfer Learning, LSTM-based Prediction, Ensemble, and Model Interpretability. The Data Preprocessing Module integrates and standardizes diverse data sources, including satellite imagery and sensor readings, ensuring high-quality input through advanced techniques like wavelet transforms. The Feature Selection Module employs a refined selection strategy to isolate the most impactful features, incorporating domain-specific insights. Transfer Learning is utilized to import and adapt knowledge from related fields, augmenting the model's predictive prowess. The LSTM-based Prediction Module is specifically engineered to capture complex temporal and spatial dependencies inherent in soil data. An Ensemble Module consolidates predictions from various models to enhance prediction robustness, and the Model Interpretability Module employs techniques such as SHAP and LIME to ensure transparency and understandability of the predictive outcomes. "SoilPredict" represents a significant stride forward in the application of machine learning to soil science, promising not only improved predictive performance but also a deeper understanding of the factors influencing soil behavior. The architecture's comprehensive approach demonstrates potential applications ranging from agricultural management to urban planning, highlighting its adaptability and scalability in facing the challenges of soil analysis