In this letter, we propose a novel, fast model for detecting fire flames and smoke using object detection and 3D classification, referred to as FastFireDet3D. This model uses NanoDet to quickly identify potential areas representing fire and smoke, followed by a novel 3D classification model based on a spatio-temporal convolutional neural network (STCNN). This two-step process allows for efficient and accurate detection. The average processing time for FastFireDet3D is approximately 40-90 ms when run on a CPU, and it achieves an accuracy improvement of 3.45% over traditional Convolutional 3D (C3D) models.