METHODS AND SYSTEMS FOR MULTI-FACTORIAL PHYSIOLOGICALLY INFORMED REFRESHMENT SELECTION USING ARTIFICIAL INTELLIGENCE

1 . A system for multi-factorial physiologically informed refreshment selection using artificial intelligence the system comprising a computing device, the computing device designed and configured to:

retrieve a biological extraction pertaining a user, wherein the biological extraction contains an element of user data;

select a nutritional machine-learning model using the biological extraction;

determine a geolocation of the user;

identify one or more providers located within the geolocation of the user;

generate a plurality of refreshment possibilities as a function of the one or more providers;

determine the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model, wherein the compatibility comprises a numerical score associated with a tolerance of the user; and

display the compatibility of the plurality of refreshment possibilities.

2 . The system of claim 1 , wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a user specification element.

3 . The system of claim 1 , wherein generating the plurality of refreshment possibilities as a function of the one or more providers comprises:

receiving a recent refreshment selection, wherein the recent refreshment selection comprises one or more ingredients and wherein each refreshment possibility of the plurality of refreshment possibilities comprises the one or more ingredients.

4 . The system of claim 1 , wherein identifying the one or more providers within the geolocation of the user further comprises selecting one or more providers as a function of a dining option.

5 . The system of claim 1 , wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a user body measurement.

6 . The system of claim 1 , wherein the computing device is further configured to rank the plurality of refreshment possibilities as a function of the compatibility of the plurality of refreshment possibilities.

7 . The system of claim 1 , wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a nutrient score.

8 . The system of claim 1 , wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a modified ingredient list.

9 . The system of claim 9 , wherein the computing device is configured to generate the modified ingredient list as a function of a user specification element.

10 . The system of claim 1 , wherein the computing device is further configured to:

receive a selection of one or more refreshment possibilities of the plurality of refreshment possibilities; and

transmit the one or more selected refreshment possibilities to a user database.

11 . A method for multi-factorial physiologically informed refreshment selection using artificial intelligence the method comprising:

retrieving, by a computing device, biological extraction pertaining a user, wherein the biological extraction contains an element of user data;

selecting, by the computing device, a nutritional machine-learning model using the biological extraction;

determining, by the computing device, a geolocation of the user;

identifying, by the computing device, one or more providers located within the geolocation of the user;

generating, by the computing device, a plurality of refreshment possibilities as a function of the one or more providers;

determining, by the computing device, the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model, wherein the compatibility comprises a numerical score associated with a tolerance of the user; and

displaying, by the computing device, the compatibility of the plurality of refreshment possibilities.

12 . The method of claim 11 , wherein determining, by the computing device, the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a user specification element.

13 . The method of claim 11 , wherein generating, by the computing device, the plurality of refreshment possibilities as a function of the one or more providers comprises:

receiving a recent refreshment selection, wherein the recent refreshment selection comprises one or more ingredients and wherein each refreshment possibility of the plurality of refreshment possibilities comprises the one or more ingredients.

14 . The method of claim 11 , wherein identifying, by the computing device, the one or more providers within the geolocation of the user further comprises selecting one or more providers as a function of a dining option.

15 . The method of claim 11 , wherein determining, by the computing device, the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a user body measurement.

16 . The method of claim 11 , further comprising ranking, by the computing device, the plurality of refreshment possibilities as a function of the compatibility of the plurality of refreshment possibilities.

17 . The method of claim 11 , wherein determining, by the computing device, the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a nutrient score.

18 . The method of claim 11 , wherein determining, by the computing device, the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a modified ingredient list.

19 . The method of claim 19 , further comprising generating, by the computing device, the modified ingredient list as a function of a user specification element.

20 . The method of claim 11 , further comprising:

receiving, by the computing device, a selection of one or more refreshment possibilities of the plurality of refreshment possibilities; and

transmitting, by the computing device, the one or more selected refreshment possibilities to a user database.