The convergence of Artificial Intelligence (AI), remote sensing, and the Internet of Things (IoT) is fundamentally transforming conventional forestry practices into smart, data-driven forest management systems. As global forest ecosystems face unprecedented threats from climate change, deforestation, illegal logging, and wildfires, integrating digital technologies has become indispensable for effective and sustainable forest stewardship. This review synthesises current advancements in smart forestry, examining how machine learning (ML), deep learning (DL), satellite remote sensing, unmanned aerial vehicles (UAVs), LiDAR, and IoT sensor networks are being deployed to enhance forest monitoring, wildfire detection, carbon stock estimation, biodiversity assessment, and forest inventory management. Key applications discussed include real-time fire detection using deep learning on drone-captured imagery, above-ground biomass estimation with LiDAR and ML algorithms, deforestation monitoring with Sentinel-2 multispectral data, and continuous ecosystem monitoring via IoT sensor arrays. Challenges, including data interoperability, computational demands, connectivity in remote areas, and ethical concerns regarding data governance, are also critically examined. The review concludes with a forward-looking perspective on digital twins, edge computing, and federated learning as next-generation enablers of precision forestry. Smart forestry technologies, when effectively integrated, offer transformative potential for meeting global sustainable development goals, achieving carbon neutrality targets, and conserving biodiversity.