Phalaenopsisorchids are highly economical ornamental potted plants. Controlling their production schedule requires information on the leaf development characteristics of the orchids.Phalaenopsisleaves affect the plant’s photosynthesis, respiration, and transpiration. The leaf growth conditions can serve as a development index for greenhouse management. The use of the growth characteristics ofPhalaenopsisleaves as the basis for greenhouse cultivation and management needs to be studied. The allometry ofPhalaenopsisleaves is worth studying. The goal of this research was to investigate the allometry ofPhalaenopsisleaves and develop prediction models of the total leaf area. Then, these total leaf area models were developed and validated. In this study, fivePhalaenopsisvarieties (amabilis, Sin-Yuan beauty, Ruey Lish beauty, Ishin KHM1095, and Sogo F1091) were selected. Each sample had five mature leaves. The lengths, widths, and areas of the sequential leaves were measured, and then the length ratios, width ratios, and area ratios were calculated. The top and bottom models were used to calculate the total leaf areas. The results indicate that no significant differences could be found in the length ratios, width ratios, and area ratios of the sequential leaves from the same variety. However, significant differences were found in these leaf characteristics between different varieties. The observation of leaf growth characteristics can be used to provide useful information forPhalaenopsismanagement. Comparing the predictive criteria of the two models, the top model had a better predictive ability than the bottom model. From a practical viewpoint, measuring the top leaf area is easier than measuring the bottom leaf area in a greenhouse operation. Comparing the effects of the sample numbers on the predictive ability of the model, the sample number of 30 was sufficient to ensure the accuracy of the total leaf area measurements. We provide an easy and accurate method to measure the total leaf area ofPhalaenopsis. The calculated values of total leaf areas can be incorporated into decision models for smart management.