The KKT Conditions, or Karush-Kuhn-Tucker Conditions, are a set of mathematical criteria used in optimization problems, particularly those involving constraints. They help determine the optimal solution for problems where you want to maximize or minimize a function while satisfying certain conditions, such as inequalities. These conditions include the primal feasibility, dual feasibility, and complementary slackness. Essentially, they provide a framework to check if a solution is optimal by relating the gradients of the objective function and the constraints. The KKT Conditions are widely used in fields like economics, engineering, and machine learning.