In Functional Data Analysis, functional data contains two types of variabilities: amplitude or vertical variability and phase or horizontal variability. Particularly for Functional Data Analysis, phase variation is a crucial noise and this is due to the lack of registration between peaks or valleys. Many registration or alignment algorithms have been proposed to reduce the phase variation between curves. However, these methods are restricted to the same fixed time intervals, that is, the functional observations are defined on the same fixed time domains. However, owing to the lack of synchronization, several functional data can be observed at different time intervals. In this study, we proposed a functional linear registration algorithm using a simple linear equation to align the functional data, which can also handle the different time intervals of functions. We demonstrated the framework using simulated data and real data to assess the algorithm’s effectiveness.