The El Niño - Southern Oscillation (ENSO) has global effects on the hydrological cycle, agriculture, ecosystems, health, and society. We present a novel non-homogeneous Hidden Markov model (NHMM) for studying the underlying dynamics of sea surface temperature anomalies (SSTA) over the region, 150E -80W, 15N-15S from Jan-1856 to Dec-2019, using the monthly SSTA data from the Kaplan Extended SST v2 product. This non-parametric Machine Learning scheme dynamically simulates and predicts the spatiotemporal evolution of ENSO patterns, including their asymmetry, long-term trends, persistence, and seasonal evolution. The model identifies five hidden states whose spatial SSTA patterns are similar to the so-called “ENSO Flavors” in the literature. From the fitted NHMM, the model shows that there are systematic trends in the frequency and persistence of the regimes over the last 160 years that may be related to changes in the mean state of basin temperature and/or global warming. We evaluated the ability of NHMM to make out of sample probabilistic predictions of the spatial structure of temperature anomalies for the period 1995-2016 using a training period from Jan-1856 to Dec-1994. The results show that NHMMs can simulate the behavior of the Niño 3.4 and Niño 1.2 regions quite well. The NHMM results over this period are comparable or superior to the commonly available ENSO prediction models, with the additional advantage of directly providing insights as to the space patterns, seasonal and longer-term trends of the SSTA in the equatorial Pacific region.