Developmental disorders impose significant public health and economic burdens on society. These conditions present substantial social, communication, and behavioral challenges throughout an individual's lifetime. It is well known that early diagnosis is crucial for enabling timely and effective interventions, which improve long-term outcomes for this population. However, significant delays in the diagnostic process in current practice cause children to miss the optimal window for early interventions, underscoring the urgent need for improving early detection by more effective approaches and processes. This dissertation aims to develop a data-driven analytical modeling framework, through a series of interrelated studies to improve early detection by enhancing the accuracy of screening tools and the efficiency of diagnosis processes, while accounting for healthcare capacity constraints. In particular, we focus on the applications of the proposed analytical and modeling framework in the clinical domains of Autism Spectrum Disorder (ASD), one of the most common developmental disorders, which has an increasing awareness and challenges in meeting the unmet needs of services in this population. In our first study, we developed machine learning prediction models to assess the risk of ASD based on clinical information for children at very young ages. In current practice, ASD screening is solely based on behavioral questionnaires, the Modified Checklist for Autism in Toddlers (M-CHAT), which does not incorporate children's clinical information, although abundant evidence in the clinical literature has shown the associations between certain clinical symptoms and ASD. To assess the predictive value of this clinical information, using commercial insurance claims data consisting of demographics, medical diagnoses, and procedures, we trained and tested models using LASSO logistic regression and random forest to predict the risk of ASD in children at the ages of 18 and 24 months, which were shown to achieve comparable accuracy with M-CHAT, or even outperform M-CHAT when differentiating inpatient and outpatient visit data. We identified key predictive features, including sex, diagnosis of other developmental disorders, respiratory infections, and gastrointestinal infections. To facilitate the integration of risk prediction models that integrate clinical information into practice, in our second study, we aimed to further improve the prediction accuracy for ASD diagnosis by building on the existing screening instrument, M-CHAT. Using real-world electronic health record (EHR) data from the Children's Hospital of Philadelphia (CHOP), we augmented the M-CHAT scores with additional demographic and clinical factors, and achieved a prediction accuracy with an area under the receiver operating characteristic curve (AUROC) of 0.83 at 18 months and 0.88 at 24 months, using a gradient boost model. To address the limitation of lacking interpretability by conventional machine learning models, we further developed a risk-calibrated supersparse linear integer model that generates the score-based prediction model. The resulting score-based checklist served as an extension to the existing items in the M-CHAT, incorporating additional features such as sex, family history, and diagnoses related to developmental disorders. This approach yielded incremental improvements in AUROC from 0.74 to 0.81 at 18 months and from 0.76 to 0.83 at 24 months, and allowed for simple interpretation and calculation of the risk score. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]