Climatic anomalies, such as heatwaves and bushfires, are increasing in number, intensity, and severity worldwide due to climate change. Bushfires are especially important in winemaking countries since smoke contamination can reach vineyards in critical periods of berry development, producing smoke contamination, which is passed to the wine as smoke taint in the winemaking process. The only alternative for winemakers to assess berry or wine contamination is by sending samples to specialised laboratories, which can be time consuming and cost-prohibitive and only sentinel plants or batches can be monitored. New and emerging technologies based on non-destructive remote sensing, such as near-infrared spectroscopy and the development of low-cost e-noses coupled with artificial intelligence (AI) tools, have been developed by our research group. The machine learning (ML) classification models developed showed high accuracy (97 - 98%) for berries, leaves and wine assessment to predict the level of smoke contamination. Furthermore, ML regression models to predict smoke-derived compounds in berries, must and wine also presented very high accuracy (R = 0.98 - 0.99). On the other hand ML models to predict consumers acceptability towards smoke tainted wines were also successfully developed (R = 0.97 - 0.98). These models can result in cost-effective and accurate technologies applicable to the vineyard and wineries to assess levels of smoke taint and associated compounds for decision-making purposes.