In general, since smoke appears before flames, smoke detection is particularly important for early fire detection systems. To detect fire-smoke using video camera is a difficult work because main characteristics of a smoke are uncertain, vague, constant patterns of shape and color. Thus, this paper proposes a new fire-smoke detection method, especially forest smoke using analyzing of temporal patterns of smoke and Fuzzy Finite Automata (FFA). To consider the smoke characteristics over time, the temporal patterns of intensity entropy, wavelet energy and motion orientation have been used for generating, multivariate probability density functions (PDFs) are applied Fuzzy Finite Automata (FFA) for smoke verification. The proposed FFA consist of a set of fuzzy states (VH, H, L, VL), and a transition mapping that describes what event can occur at which state and resulting new state. For smoke verification, FFA is most appropriate method in case variables are time-dependent and uncertain. The proposed algorithm is successfully applied to various fire-smoke videos and shows a better detection performance.