Forest fires pose a significant threat to wildlife and human life, highlighting the need for an effective early prediction mechanism. Due to the lack of collecting actual forest-fire data, we emulate a small-scale forest fire to gather data that mimics large-scale forest fires. Our focus is to obtain the valuable chronological set of fire development scenarios via a specially deployed Wireless Sensor Networks (WSNs) around the emulated fire bed to sense the fire progress. We labeled each data record with a camera and timestamp for accurate scenario association. The sensed dataset of records is refined to maintain its integrity at the security and validity levels and then used to train machine learning models adding the “smart” and “secure” components to our deployed WSN. Smart and secure components are necessary in WSNs to enhance efficiency, and adaptability, and protect sensitive data from unauthorized access and cyber threats. Our innovative smart and secure wireless sensing system allowed us to obtain useful experimentation classified scenarios, utilizing a wide range of prominent machine learning models compared to the traditional systems. Such scenarios cover three major areas of forest-fire dynamics (FFD): no-sign-of-fire (NSF), early fire warning (EFW), and fire exists (FE). EFW includes two critical scenarios, “lighting” and “thunder”, which are particularly important as they may occur just before the ignition of most forest fires. Hence, EFW is crucial for early prediction, versus the “late” and costly detection of forest fires with other fire scenarios, i.e. FE. Our system’s early prediction accuracy (around 95%) is very promising with a solid potential to significantly improve fire management strategies, preventing large-scale devastation.