BACKGROUND: Internet-based Cognitive Behavioral Therapy (ICBT) is effective in treating anxiety disorders, yet there is room for improvement in treatment response and reduction in dropout rates. This study proposes a personalized, modular ICBT intervention that leverages the extended evolutionary meta-model to provide a dynamic and adaptive treatment approach, aiming to enhance usability and efficacy. METHODS: The trial will be conducted in two phases. Phase I involves 182 participants who will undergo a 30-day ecological momentary assessment to record functional processes and anxiety levels three times a day. The data collected will help in identifying key functional predictors of anxiety for each participant through group iterative multiple model estimation. In Phase II, participants who complete Phase I will be randomized into three groups: personalized CBT, standard CBT, and a waiting list. Outcome measures will include Brief Symptom Inventory, specific measures of anxiety, usability metrics, and dropout rates. Assessments will be conducted at baseline, immediately post-treatment, and at 1- and 3-month follow-ups. A linear mixed model will be utilized to analyze the data and determine the intervention's efficacy. DISCUSSION: Anticipated outcomes from this study include advancements in personalized CBT for anxiety disorders, contributing valuable insights into their potential benefits and addressing existing challenges in the field.