An estimated 300 million people worldwide suffer from asthma, and this number is expected to increase to 400 million by 2025. Approximately 250,000 people die prematurely each year from asthma out of which, almost all deaths are avoidable. Most of these deaths occur because the patients are unaware of their asthmatic morbidity. If detected early, asthmatic mortality rate can be reduced by 78%, provided that the patients carry appropriate medication for the same and/or are in lose vicinity to medical equipment like nebulizers. This study focuses on the development and valuation of algorithms to diagnose asthma through symptom intensive questionary, clinical data and medical reports. Machine Learning Algorithms like Back-propagation model, Context Sensitive Auto-Associative Memory Neural Network Model, C4.5 Algorithm, Bayesian Network and Particle Swarm Optimization have been employed for the diagnosis of asthma and later a comparison is made between their respective prospects. All algorithms received an accuracy of over 80%. However, the use of Auto Associative Memory Model (on a layered Artificial Neural Network) displayed much better results. It reached to an accuracy of over 90% and an inconclusive diagnosis rate of less than 1% when trained with adequate data. In the end, naïve mobile based applications were developed on Android and iOS that made use of the self-training auto associative memory model to achieve an accuracy of nearly 94.2%.