Loss minimization is a fundamental objective in machine learning and optimization, aiming to reduce the difference between predicted and actual outcomes by adjusting model parameters. It is achieved through iterative algorithms that seek to find the optimal parameter values that minimize a predefined loss function, enhancing model accuracy and performance.