Abstract
Mental health challenges are increasingly prevalent across all age groups, yet access to professional support remains limited in many regions. In Malawi, a country with an estimated population of 21.9 million as of 2025, mental health disorders such as depression, anxiety, and stress-related conditions are a growing public health concern. It is estimated that 20–30% of the general population experience mental health conditions, while postnatal depression affects more than 40% of adolescent mothers. Despite the magnitude of these issues, the country has only four psychiatrists serving the entire population, contributing to a severe treatment gap that continues to widen annually.
The situation is compounded by alarming suicide trends 281 suicide deaths were recorded in the first half of 2024 alone, with the majority being male. Moreover, social and economic challenges like food insecurity have a measurable impact on psychological well-being, with even a small increase in food insecurity significantly raising the prevalence of mental illness among vulnerable communities. These statistics highlight an urgent need for innovative, scalable, and accessible mental health solutions, particularly in low-resource settings where traditional infrastructure is lacking.
Artificial Intelligence offers a promising pathway to extend mental health support across such underserved regions. AI-powered chatbots can simulate human-like conversations, providing users with immediate, empathetic, and anonymous emotional assistance and guided interventions. These systems are cost-effective, available 24/7, and scalable, making them ideal for environments like Malawi where human mental health resources are scarce and overburdened.
This research presents the development of a Mental Health Support System using an AI chatbot designed specifically for the Malawian context. The system utilizes Natural Language Processing (NLP) to identify emotional cues and deliver context-aware responses. Features include multilingual interaction, emotional state detection, and future integration with wearable health devices. It aims to promote mental resilience and improve early psychological intervention.