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Address verification is a critical and challenging task for businesses worldwide, given that every country has its address formatting conventions. This is particularly relevant in industries such as imported food safety, where prompt safety measures are vital to safeguard people when food safety issues arise. Hence, we developed a deep learning and Google Geocoding-based technique to parse, validate, and standardize the address accurately. Our proposed model utilizes a deep learning algorithm to identify an address's components, such as the street name, district, city, and state/province then validate them using Google Geocoding. However, our initial model did not yield great results for countries with well-established postal/zip code systems. To resolve this issue, we improved the model by adding an extra administrative level - the Postal Code - specifically for such countries. To evaluate the efficacy of our new model, we compared multiple metrics such as accuracy, precision, recall, and F1 score of the old and new models. The dataset used to test the new model comprised addresses from manufacturers in the United Kingdom, the United States, and Australia. The results showed that our new model outperformed the original model with better accuracy when applied to real address data. With the addition of this extra administrative level, the performance of the verification process for global addresses is improved significantly.