Many complex real-life decision-making problems have been discussed using intuitionistic fuzzy distance measures. Sundry intuitionistic fuzzy distance measuring techniques have been developed. Ejegwa et al. developed some intuitionistic fuzzy distance measures, including the three intuitionistic fuzzy parameters: membership grade, Non-Membership (NM) grade, and hesitation grade. Albeit, Ejegwa et al.'s techniques did not consider the weights of the elements of the underlying sets upon which the intuitionistic fuzzy sets are defined. This omission could certainly affect the distance outputs. As a sequel to this setback, we develop a weighted intuitionistic fuzzy distance measure, where the weights are computed from the intuitionistic fuzzy values to enhance reliable results. In addition, the new weighted intuitionistic fuzzy distance measure is applied to discuss a pattern recognition problem to ascertain the patterns associated more closely with an unknown pattern. In addition, the new weighted intuitionistic fuzzy distance measure is applied to medical diagnosis to ascertain a patient's medical problem given certain symptoms. Finally, the superiority of the newly developed weighted intuitionistic fuzzy distance measure is shown comparatively concerning the existing intuitionistic fuzzy distance measures.