The global palm oil industry faces mounting pressure to enhance productivity while addressing sustainability challenges inherent to conventional cultivation practices. Traditional plantation management, heavily reliant on manual labor and empirical decision-making, often results in operational inefficiencies, resource wastage, and suboptimal yield forecasting. However, recent advancements in big data analytics, Internet of Thing (IoT)-enabled sensors, and artificial intelligence (AI) are driving a paradigm shift toward precision agriculture in oil palm plantation. By integrating real-time data streams from remote sensing, drone imagery, and automated monitoring systems, modern plantations can optimise agronomic inputs, detect biotic stresses early, and improve harvest predictability with unprecedented accuracy. Furthermore, computer vision and machine learning algorithms are transforming quality assessment protocols, replacing subjective manual grading with objective, data-driven ripeness classification. This technological convergence not only enhances operational efficiency and profitability but also fosters sustainable intensification by minimising environmental footprints. As the sector embraces these innovations, big data analytics emerges as a critical enabler of a more resilient and ecologically balanced palm oil value chain, aligning economic objectives with long-term sustainability imperatives.