As Rob writes, I am not sure why you believe that smoothing is limited to one-step-ahead forecasts.
Jan Feb Mar Apr May Jun Jul Aug
1961 453.4977 429.3906 467.0361 503.2574 512.3395 571.8880 652.6095 637.4623
Sep Oct Nov Dec
1961 539.7548 490.7250 424.4593 469.5315
Exponential Smoothing and ARIMA are indeed the first forecasting methods you will learn about, but of course there are many more. Some are for specific use cases, e.g., Croston's method for intermittent demands, or Bass models for forecasting new product diffusion. Others are more general, like regression or Dynamic Linear Models (DLMs) to model causal effects, or Singular Spectrum Analysis, or Neural Networks, or even Random Forests - pretty much any Machine Learning method for numerical prediction has already been applied to time series.
You might want to look into a forecasting textbook, e.g., Ord, Fildes & Kourentzes Principles of Business Forecasting (2nd ed., 2017). However, textbooks will again have a strong focus on the "classical" methods. Alternatively, you could browse the abstracts at the International Symposium on Forecasting to get an idea what forecasting researchers are looking at these days.