Cardiovascular Disease (CVD) continues to be a prominent cause of mortality worldwide, posing substantial difficulties in identifying and treating it at an early stage. The intricacy of CVD stems from the interaction of genetic, environmental, and lifestyle variables that contribute to its development. Conventional diagnostic approaches often face challenges in effectively integrating and analysing the many data linked to cardiovascular disease (CVD). In order to tackle these difficulties, we suggest using a Transfer Learning approach that is specifically developed to improve the prediction of early stage cardiovascular disease (CVD). Our methodology entails using pre-trained models via transfer learning, where we modify them for cardiovascular disease (CVD) prediction tasks by fine-tuning them with data particular to the CVD domain. We assessed our technique by using an open-source CVD dataset obtained from Kaggle. The dataset has a diverse array of clinical and demographic data that is pertinent to cardiovascular health. Evaluation of performance was conducted utilising essential measures, such as the confusion matrix and accuracy. The confusion matrix offered comprehensive insights into the classification performance of the model, namely identifying true positives, false positives, true negatives, and false negatives. The accuracy metric was used to measure the overall efficacy of the model in accurately predicting instances of cardiovascular disease (CVD). The Transfer Learning-based framework demonstrated superior performance with an accuracy of 82%, surpassing the classic Convolutional Neural Network (CNN) model, which attained an accuracy of 77%. The increased accuracy demonstrates that the Transfer Learning technique is more efficient in using pre-existing information and customising it to address the particular issues of CVD prediction. The enhanced efficacy highlights the benefits of using transfer learning to manage intricate and varied data, resulting in more precise and dependable early identification of cardiovascular illness.