A method of determining and mitigating wax risk of a hydrocarbon composition, the method comprising: obtaining a sample of the hydrocarbon composition; analyzing the sample of the hydrocarbon composition to determine one or more attributes of the sample of the hydrocarbon composition, wherein the one or more attributes preferably include at least one of Total Acid Number, American Petroleum Institute gravity, specific gravity, SARA (saturates, aromatics, resins, asphaltenes), Colloid Instability Index, viscosity, rheology, wax content, heavy wax content, Wax Appearance Temperature , and Pour Point; developing a first predictive model of a hydrocarbon production, transportation, refining, storage, processing, and/or distribution process based on the analysis of the sample of the hydrocarbon composition; determining first wax risks based on the first predictive model, wherein the determined first wax risks preferably include one or more of wax content, heavy wax content, Wax Appearance Temperature, Pour Point, and wax deposition potential, and wherein the first predictive model is developed using one or more machine learning algorithms; developing , using the one or more machine learning algorithms, a pipe flow model for a pipeline associated with the hydrocarbon production, transportation, refining, storage processing, and/or distribution process, wherein the pipe flow model is a second predictive model developed using real-time and stored parameters and operating conditions of the pipeline; determining second wax risks for the pipeline based on the pipe flow model and the determined first wax risks; and mitigating the determined first and/or second wax risks by introducing one or more chemical additives during at least one of the production, transportation, storage, processing, and/or distribution of the hydrocarbon composition. obtaining a sample of the hydrocarbon composition; analyzing the sample of the hydrocarbon composition to determine one or more attributes of the sample of the hydrocarbon composition, wherein the one or more attributes preferably include at least one of Total Acid Number, American Petroleum Institute gravity, specific gravity, SARA (saturates, aromatics, resins, asphaltenes), Colloid Instability Index, viscosity, rheology, wax content, heavy wax content, Wax Appearance Temperature , and Pour Point; developing a first predictive model of a hydrocarbon production, transportation, refining, storage, processing, and/or distribution process based on the analysis of the sample of the hydrocarbon composition; determining first wax risks based on the first predictive model, wherein the determined first wax risks preferably include one or more of wax content, heavy wax content, Wax Appearance Temperature, Pour Point, and wax deposition potential, and wherein the first predictive model is developed using one or more machine learning algorithms; developing , using the one or more machine learning algorithms, a pipe flow model for a pipeline associated with the hydrocarbon production, transportation, refining, storage processing, and/or distribution process, wherein the pipe flow model is a second predictive model developed using real-time and stored parameters and operating conditions of the pipeline; determining second wax risks for the pipeline based on the pipe flow model and the determined first wax risks; and mitigating the determined first and/or second wax risks by introducing one or more chemical additives during at least one of the production, transportation, storage, processing, and/or distribution of the hydrocarbon composition. <EOS>