Child and Adolescent mental health disorders (MHD) are a global issue that have significant impacts on individuals, families, society, and the economy. It is important to identify specific subgroups within children population and traditional methods of identifying subgroups using single informant may not be effective, leading to inaccurate findings. Multilevel latent class models are not yet explored for clustering schools with nested children exhibiting similar behavior patterns. The objectives of this thesis are: i) to analyze the mental health patterns among children using multiple informants and compare with those obtained from single informant; ii) to capture the heterogeneity of mental health patterns across schools and cluster schools based on these patterns; and iii) to assess the effect of school-level and individual-level factors on clusters of schools and mental health patterns of children respectively. We employed a proposed Latent Class Analysis (LCA) technique to classify students into latent mental health patterns, integrating assessments provided by both teachers and students in Manitoba Grade 5 Mental Health Survey. We extended the proposed LCA model to accommodate the nested structure of the data by specifying categorical latent variable to cluster schools. Additionally, we performed multinomial logistic regression to assess the effects of school-level and student-level factors. We identified six mental health classes (high, moderately high, medium, mild, mild internalizing and low risk) for each informant and three mental health patterns: high-risk, low-risk, and self-reported risk among students integrating both reports. Three mental health clusters: high-risk, low-risk and student-reported risk clusters among schools were identified. Male, Canadian-born, and those engaged in bullying activities reported by teachers had higher odds of being in high-risk pattern and a higher prevalence of bullying in school settings was associated with higher odds of being in the high-risk and student-reported risk school clusters. The proposed model provides a classification technique to analyze data from multiple informants within hierarchical structure by considering the possible correlations among assessments and within higher-level groups. The mental health patterns identified in this study guide policymakers in developing teacher training programs, offering insights into tailored interventions for students' specific needs.