Soil organic carbon (SOC) is an important parameter of soils and a critical factor in global carbon cycling. The accurate monitoring and modelling of SOC are essential for assessing soil fertility, facilitating sustainable land management, and mitigating climate change. This research explores the integration of machine learning (ML) approaches with soil, terrain and remotely sensed data to enhance SOC estimation. Various ML models, including Neural Networks (NNs), Random Forests (RFs), Support Vector Machines (SVMs) and Decision Trees (DTs), were trained and evaluated using a dataset comprising soil laboratory data, Sentinel-2 spectral indices, climate data and topographic features. Feature selection techniques were applied to indicate the most important predictors, improving model performance and interpretability. The results demonstrate the potential of ML-driven approaches to achieve high accuracy in SOC prediction. This research highlights the advantages of leveraging big data and artificial intelligence in soil monitoring, providing a scalable and cost-effective framework for SOC assessment in agricultural and environmental applications.