Normalization is the first step of any MADM approach. It is used to standardize data and eliminate redundancies, inconsistencies, and anomalies by adjusting values to fall within a predetermined range or scale. In this chapter, we will explore importance, purpose, step-by-step computation, types, advantages, disadvantages, and applications in different areas of the domain of different approaches to normalization. This chapter also has a discussion of utility theory and its different types that is geared toward facilitating effective decision-making processes.