Cardiovascular diseases remain one of the leading causes of death worldwide, underscoring the urgent need for reliable and efficient testing techniques. While deep learning and machine learning have significantly improved medical diagnoses, their effectiveness is often limited for rare diseases due to a scarcity of data, which arises from their reliance on extensive datasets. Meta-learning presents a viable solution as it enables models to quickly adapt to new tasks with minimal training data, making it particularly effective for rare disease classification. In this proposed study, we explore the potential of meta-learning in cardiology, specifically focusing on heart attack classification. To enhance predictive accuracy with limited data, we employ deep learning techniques such as model-agnostic meta-learning (MAML) and feature extraction using VGG16. Our experimental results demonstrate a high accuracy of 92.67% on specialized datasets, indicating that meta-learning can effectively address data limitations and enhance diagnostic accuracy. By leveraging meta-learning in cardiovascular diagnoses, we could potentially improve disease management. The performance of this cardio-MAML model is expected to improve as more diverse and extensive datasets become available in the future. This study adds to the growing body of research on meta-learning in healthcare, emphasizing its potential to transform early detection and diagnosis in cardiology.