The terms dependent variable and independent variable come from experimental research, where they represent the necessary components of an experiment:
An experiment is the systematic manipulation/variation of a independent variable (IV) to observe the influence of this manipulation on a dependent variable (DV) while controlling for confounding variables.
Thus, both have a specific definition and should not be used interchangeably with the other terms on your list. Terms such as treatment are simply more specific names for those two components. For example, treatment is usually used in medical science instead of the term independent variable.
A covariate is defined as a variable that varies with another variable (co-variation). It is the broadest term and its specific meaning varies on the statistical model. Usually, it represents a variable that is not of main interest but confounds, moderates or mediates a relationship.
The terms predictor and outcome are terms that denote components of a statistical model. A predictor is supposed to predict variation in another variable (the outcome). Prediction implies a believed causal relationship between predictor and outcome, but in fact no other assumptions or background information (in contrast to IV/DV) is implied. Every IV is a predictor but not every predictor is an IV. They are often used in contrast to IV/DV to indicate that an analysis uses observational data and not experimental data as defined above. Regressor denotes a predictor in regression models.
Feature and target are jargon for predictor and outcome in machine learning models. I can only guess their origin but I believe they were introduced by image recognition. The features of an object determine its classification.
Factor can have multiple meanings, depending on the model in which this term appears (e.g. an ANOVA model, factor analysis etc.)