What are some appropriate models to use for inventory forecast based on consumption history or trends? I am working on an inventory management system where I have daily/monthly/yearly consumption history for a particular item, which may or may not follow a repeating trend. In order to forecast demands within an upcoming time-frame, which model / distributions are recommended?

Data Format: Data is a series of (x, y) pairs where x is a timestamp and y is count of consumption.

As far as I studied, random forest or tree based models do not handle trend patterns effectively, while linear models do not seem to be appropriate either given the fact that data do not seem to be following a linear pattern.

Is this problem solvable using a single model? Should Linear Regression models be used for this problem?
The model you are looking for is called ARIMA, which are time series models where you can obtain trends, cycles, etc.

The ARIMA models lets you model your univariate data by detecting monthly/annual cycles, natural growth (trends) for a variable depending on the history of the same variable.